Here I am importing the file which contains monthly PM level estimates by satellite at nearest lon/lat to Simmons patient residential addresses. These are linked to the patient ID.
outfile1 <- here("Simmons_fILD_2000_2018_PM25_2021_09_08.xlsx")
PM <- read_excel(outfile1)
Here I am importing the file which I used for my ADI work that contains the baseline clinical and demographic data for 1425 patients who have ADI
outfile2 <- here("Simmons_fILDPts_BaselineData_2022_03_28.xlsx")
Simm <- read_excel(outfile2)
PM <- PM %>% dplyr::select(!c(nrow, dist, lon, lat))
colnames(PM)
## [1] "ID" "PM25_jan00" "PM25_feb00" "PM25_mar00" "PM25_apr00"
## [6] "PM25_may00" "PM25_jun00" "PM25_jul00" "PM25_aug00" "PM25_sep00"
## [11] "PM25_oct00" "PM25_nov00" "PM25_dec00" "PM25_jan01" "PM25_feb01"
## [16] "PM25_mar01" "PM25_apr01" "PM25_may01" "PM25_jun01" "PM25_jul01"
## [21] "PM25_aug01" "PM25_sep01" "PM25_oct01" "PM25_nov01" "PM25_dec01"
## [26] "PM25_jan02" "PM25_feb02" "PM25_mar02" "PM25_apr02" "PM25_may02"
## [31] "PM25_jun02" "PM25_jul02" "PM25_aug02" "PM25_sep02" "PM25_oct02"
## [36] "PM25_nov02" "PM25_dec02" "PM25_jan03" "PM25_feb03" "PM25_mar03"
## [41] "PM25_apr03" "PM25_may03" "PM25_jun03" "PM25_jul03" "PM25_aug03"
## [46] "PM25_sep03" "PM25_oct03" "PM25_nov03" "PM25_dec03" "PM25_jan04"
## [51] "PM25_feb04" "PM25_mar04" "PM25_apr04" "PM25_may04" "PM25_jun04"
## [56] "PM25_jul04" "PM25_aug04" "PM25_sep04" "PM25_oct04" "PM25_nov04"
## [61] "PM25_dec04" "PM25_jan05" "PM25_feb05" "PM25_mar05" "PM25_apr05"
## [66] "PM25_may05" "PM25_jun05" "PM25_jul05" "PM25_aug05" "PM25_sep05"
## [71] "PM25_oct05" "PM25_nov05" "PM25_dec05" "PM25_jan06" "PM25_feb06"
## [76] "PM25_mar06" "PM25_apr06" "PM25_may06" "PM25_jun06" "PM25_jul06"
## [81] "PM25_aug06" "PM25_sep06" "PM25_oct06" "PM25_nov06" "PM25_dec06"
## [86] "PM25_jan07" "PM25_feb07" "PM25_mar07" "PM25_apr07" "PM25_may07"
## [91] "PM25_jun07" "PM25_jul07" "PM25_aug07" "PM25_sep07" "PM25_oct07"
## [96] "PM25_nov07" "PM25_dec07" "PM25_jan08" "PM25_feb08" "PM25_mar08"
## [101] "PM25_apr08" "PM25_may08" "PM25_jun08" "PM25_jul08" "PM25_aug08"
## [106] "PM25_sep08" "PM25_oct08" "PM25_nov08" "PM25_dec08" "PM25_jan09"
## [111] "PM25_feb09" "PM25_mar09" "PM25_apr09" "PM25_may09" "PM25_jun09"
## [116] "PM25_jul09" "PM25_aug09" "PM25_sep09" "PM25_oct09" "PM25_nov09"
## [121] "PM25_dec09" "PM25_jan10" "PM25_feb10" "PM25_mar10" "PM25_apr10"
## [126] "PM25_may10" "PM25_jun10" "PM25_jul10" "PM25_aug10" "PM25_sep10"
## [131] "PM25_oct10" "PM25_nov10" "PM25_dec10" "PM25_jan11" "PM25_feb11"
## [136] "PM25_mar11" "PM25_apr11" "PM25_may11" "PM25_jun11" "PM25_jul11"
## [141] "PM25_aug11" "PM25_sep11" "PM25_oct11" "PM25_nov11" "PM25_dec11"
## [146] "PM25_jan12" "PM25_feb12" "PM25_mar12" "PM25_apr12" "PM25_may12"
## [151] "PM25_jun12" "PM25_jul12" "PM25_aug12" "PM25_sep12" "PM25_oct12"
## [156] "PM25_nov12" "PM25_dec12" "PM25_jan13" "PM25_feb13" "PM25_mar13"
## [161] "PM25_apr13" "PM25_may13" "PM25_jun13" "PM25_jul13" "PM25_aug13"
## [166] "PM25_sep13" "PM25_oct13" "PM25_nov13" "PM25_dec13" "PM25_jan14"
## [171] "PM25_feb14" "PM25_mar14" "PM25_apr14" "PM25_may14" "PM25_jun14"
## [176] "PM25_jul14" "PM25_aug14" "PM25_sep14" "PM25_oct14" "PM25_nov14"
## [181] "PM25_dec14" "PM25_jan15" "PM25_feb15" "PM25_mar15" "PM25_apr15"
## [186] "PM25_may15" "PM25_jun15" "PM25_jul15" "PM25_aug15" "PM25_sep15"
## [191] "PM25_oct15" "PM25_nov15" "PM25_dec15" "PM25_jan16" "PM25_feb16"
## [196] "PM25_mar16" "PM25_apr16" "PM25_may16" "PM25_jun16" "PM25_jul16"
## [201] "PM25_aug16" "PM25_sep16" "PM25_oct16" "PM25_nov16" "PM25_dec16"
## [206] "PM25_jan17" "PM25_feb17" "PM25_mar17" "PM25_apr17" "PM25_may17"
## [211] "PM25_jun17" "PM25_jul17" "PM25_aug17" "PM25_sep17" "PM25_oct17"
## [216] "PM25_nov17" "PM25_dec17" "PM25_jan18" "PM25_feb18" "PM25_mar18"
## [221] "PM25_apr18" "PM25_may18" "PM25_jun18" "PM25_aug18" "PM25_sep18"
## [226] "PM25_oct18" "PM25_nov18" "PM25_dec18"
PM <- PM %>%
pivot_longer(cols=c(2:228), names_to="PM_date", names_prefix="PM25_")
PMx <- PM
PMx$PM_date <- gsub("jan", "01-01-20", PMx$PM_date)
PMx$PM_date <- gsub("feb", "01-02-20", PMx$PM_date)
PMx$PM_date <- gsub("mar", "01-03-20", PMx$PM_date)
PMx$PM_date <- gsub("apr", "01-04-20", PMx$PM_date)
PMx$PM_date <- gsub("may", "01-05-20", PMx$PM_date)
PMx$PM_date <- gsub("jun", "01-06-20", PMx$PM_date)
PMx$PM_date <- gsub("jul", "01-07-20", PMx$PM_date)
PMx$PM_date <- gsub("aug", "01-08-20", PMx$PM_date)
PMx$PM_date <- gsub("sep", "01-09-20", PMx$PM_date)
PMx$PM_date <- gsub("oct", "01-10-20", PMx$PM_date)
PMx$PM_date <- gsub("nov", "01-11-20", PMx$PM_date)
PMx$PM_date <- gsub("dec", "01-12-20", PMx$PM_date)
PMx$PM_date <- format(as.Date(PMx$PM_date, format="%d-%m-%Y"),"%Y-%m-%d")
PMx$PM_date <- as.Date(PMx$PM_date)
PM <- PMx
rm(PMx)
Extracting year of diagnosis and year of death/transplant/censoring
#Start with the year of diagnosis
Simm <- Simm %>%
mutate(dx_yrmo = format(as.Date(Simm$dx_date, format="%Y-%m-%d"),"%Y-%m"))
Simm <- Simm %>%
mutate(dx_yr = format(as.Date(Simm$dx_date, format="%Y-%m-%d"),"%Y"))
Simm$dx_yr <- as.numeric(Simm$dx_yr)
#Then the year of death or lung transplant
Simm <- Simm %>%
mutate(deathORtx_date = if_else(!is.na(tx_date), tx_date, death_date))
Simm <- Simm %>%
mutate(deathORtx_yrmo = format(as.Date(Simm$deathORtx_date, format="%Y-%m-%d"),"%Y-%m"))
#Then the year the records were last updated (i.e. year of censoring)
Simm <- Simm %>%
mutate(DeathTxCensor_date = if_else(!is.na(deathORtx_date), deathORtx_date, last_updated))
Simm <- Simm %>%
mutate(censor_yrmo = format(as.Date(Simm$DeathTxCensor_date, format="%Y-%m-%d"),"%Y-%m"))
Simm <- Simm %>% dplyr::select(!c(ADI_state, lat, lon, City, State, zip5, UPMC_lastvisit, Simmons_lastvisit, pkyrs, fev1_pre, fev1_pct, fvc_pre, dlco_pre, pft_timefromdx, ethnicity, dx_type, deathORtx_date, deathORtx_yrmo))
Simm <- Simm %>%
mutate_at(c("dob","death_date", "last_updated", "tx_date", "dx_date", "consent_date", "pft_date", "DeathTxCensor_date"), as.Date)
str(Simm)
## tibble [1,425 × 32] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:1425] 1097 1405 2796 4742 7898 ...
## $ dob : Date[1:1425], format: "1937-08-15" "1956-07-21" ...
## $ ADI_nat : num [1:1425] 67 73 68 62 21 34 25 23 68 NA ...
## $ death_date : Date[1:1425], format: "2008-12-09" NA ...
## $ last_updated : Date[1:1425], format: "2021-01-27" "2021-01-27" ...
## $ tx_date : Date[1:1425], format: NA "2019-04-18" ...
## $ dx_date : Date[1:1425], format: "2002-01-24" "1999-07-26" ...
## $ consent_date : Date[1:1425], format: "2003-02-27" "2003-10-30" ...
## $ pft_date : Date[1:1425], format: "2002-01-18" "1999-07-26" ...
## $ fvc_pct : num [1:1425] 31.9 53.6 82.5 47.6 62 ...
## $ dlco_pct : num [1:1425] NA 38.1 98.2 65.3 42 ...
## $ status : num [1:1425] 1 2 0 0 0 1 1 1 1 0 ...
## $ age_dx : num [1:1425] 64.4 44.7 42.3 45.5 45.2 ...
## $ time_censoring : num [1:1425] 6.72 19.93 20.43 19.16 1.45 ...
## $ time_death : num [1:1425] 6.87 NA NA NA NA ...
## $ time_tx : num [1:1425] NA 18 NA NA NA ...
## $ time_deathORtx : num [1:1425] 6.87 18.05 NA NA NA ...
## $ time_DeathTxCensor: num [1:1425] 6.87 18.05 20.43 19.16 1.45 ...
## $ sex : chr [1:1425] "F" "F" "M" "F" ...
## $ race : chr [1:1425] "B" "W" "W" "W" ...
## $ died : chr [1:1425] "1" "0" "0" "0" ...
## $ txed : chr [1:1425] "0" "1" "0" "0" ...
## $ deadORtx : chr [1:1425] "1" "1" "0" "0" ...
## $ dx : chr [1:1425] "SSC_ILD" "SSC_ILD" "RA_ILD" "MCTD" ...
## $ smokeHx : chr [1:1425] NA "Former" "Never" NA ...
## $ dich_Race : chr [1:1425] "Non-White" "White" "White" "White" ...
## $ dich_smoking : chr [1:1425] NA "Ever" "Never" NA ...
## $ dx_group : chr [1:1425] "CTD-ILD" "CTD-ILD" "CTD-ILD" "CTD-ILD" ...
## $ dx_yrmo : chr [1:1425] "2002-01" "1999-07" "2003-10" "1999-12" ...
## $ dx_yr : num [1:1425] 2002 1999 2003 1999 2015 ...
## $ DeathTxCensor_date: Date[1:1425], format: "2008-12-09" "2019-04-18" ...
## $ censor_yrmo : chr [1:1425] "2008-12" "2019-04" "2021-01" "2021-01" ...
Need to correct smoking variables
Simm$smokeHx <- as.character(Simm$smokeHx)
Simm <- Simm %>% mutate(smokeHx1=if_else(is.na(smokeHx), "Unknown", smokeHx))
#now need to make new dich_smoking category
Simm$dich_smoking <- as.character(Simm$dich_smoking)
Simm <- Simm %>% mutate(dich_smoking1=if_else(is.na(dich_smoking),"Unknown", dich_smoking))
Now need to remove old smoking variables and rename new ones
Simm <- Simm %>% dplyr::select(-c(smokeHx, dich_smoking))
Simm <- Simm %>% rename(c("smokeHx"="smokeHx1", "dich_smoking"="dich_smoking1"))
Simm$smokeHx <- as.factor(Simm$smokeHx)
Simm$dich_smoking <- as.factor(Simm$dich_smoking)
Simm$smokeHx <- fct_relevel(Simm$smokeHx, c("Never","Former","Always","Unknown"))
Simm$dich_smoking <- fct_relevel(Simm$dich_smoking, c("Never","Ever","Unknown"))
Need to correct other factor variables
Simm$sex <- fct_relevel(Simm$sex, c("M","F"))
Simm$race <- fct_relevel(Simm$race, c("W","B","A","N","U"))
Simm$dich_Race <- fct_relevel(Simm$dich_Race, c("White","Non-White"))
Simm$dx <- fct_relevel(Simm$dx, c("IPF"))
Simm$dx_group <- fct_relevel(Simm$dx_group, c("IPF"))
Simm <- Simm %>% mutate_at(c("status","died", "txed", "deadORtx"), as.factor)
Simm <- Simm %>% mutate(dx_IPF=ifelse(dx=="IPF", "IPF", "not_IPF"))
Simm$dx_IPF <- fct_relevel(Simm$dx_IPF, c("IPF"))
Creating this empirical cumulative distribution will allow us to combine the analyses of all three cohorts even though the measurements for disadvantage are different between the three.
plot(ecdf(Simm$ADI_nat))
Simm$disadv <- ecdf(Simm$ADI_nat)(Simm$ADI_nat)
Take down Simmons dataset to the 1424 patients with complete data (currently at 1425)
IDs <- as.data.table(unique(PM$ID))
IDs <- IDs %>% rename("ID"="V1")
Simm <- left_join(IDs, Simm, by="ID")
Simm <- Simm %>% mutate(days_DeathTxCensor=(time_DeathTxCensor*365.25))
Longest time_DeathTxCensor= 20.427105yrs =7461days
30-day longest time interval would be 7470 days
start <- seq(1, 7441, by = 30)
start
## [1] 1 31 61 91 121 151 181 211 241 271 301 331 361 391 421
## [16] 451 481 511 541 571 601 631 661 691 721 751 781 811 841 871
## [31] 901 931 961 991 1021 1051 1081 1111 1141 1171 1201 1231 1261 1291 1321
## [46] 1351 1381 1411 1441 1471 1501 1531 1561 1591 1621 1651 1681 1711 1741 1771
## [61] 1801 1831 1861 1891 1921 1951 1981 2011 2041 2071 2101 2131 2161 2191 2221
## [76] 2251 2281 2311 2341 2371 2401 2431 2461 2491 2521 2551 2581 2611 2641 2671
## [91] 2701 2731 2761 2791 2821 2851 2881 2911 2941 2971 3001 3031 3061 3091 3121
## [106] 3151 3181 3211 3241 3271 3301 3331 3361 3391 3421 3451 3481 3511 3541 3571
## [121] 3601 3631 3661 3691 3721 3751 3781 3811 3841 3871 3901 3931 3961 3991 4021
## [136] 4051 4081 4111 4141 4171 4201 4231 4261 4291 4321 4351 4381 4411 4441 4471
## [151] 4501 4531 4561 4591 4621 4651 4681 4711 4741 4771 4801 4831 4861 4891 4921
## [166] 4951 4981 5011 5041 5071 5101 5131 5161 5191 5221 5251 5281 5311 5341 5371
## [181] 5401 5431 5461 5491 5521 5551 5581 5611 5641 5671 5701 5731 5761 5791 5821
## [196] 5851 5881 5911 5941 5971 6001 6031 6061 6091 6121 6151 6181 6211 6241 6271
## [211] 6301 6331 6361 6391 6421 6451 6481 6511 6541 6571 6601 6631 6661 6691 6721
## [226] 6751 6781 6811 6841 6871 6901 6931 6961 6991 7021 7051 7081 7111 7141 7171
## [241] 7201 7231 7261 7291 7321 7351 7381 7411 7441
end <- seq(30, 7470, by = 30)
end
## [1] 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450
## [16] 480 510 540 570 600 630 660 690 720 750 780 810 840 870 900
## [31] 930 960 990 1020 1050 1080 1110 1140 1170 1200 1230 1260 1290 1320 1350
## [46] 1380 1410 1440 1470 1500 1530 1560 1590 1620 1650 1680 1710 1740 1770 1800
## [61] 1830 1860 1890 1920 1950 1980 2010 2040 2070 2100 2130 2160 2190 2220 2250
## [76] 2280 2310 2340 2370 2400 2430 2460 2490 2520 2550 2580 2610 2640 2670 2700
## [91] 2730 2760 2790 2820 2850 2880 2910 2940 2970 3000 3030 3060 3090 3120 3150
## [106] 3180 3210 3240 3270 3300 3330 3360 3390 3420 3450 3480 3510 3540 3570 3600
## [121] 3630 3660 3690 3720 3750 3780 3810 3840 3870 3900 3930 3960 3990 4020 4050
## [136] 4080 4110 4140 4170 4200 4230 4260 4290 4320 4350 4380 4410 4440 4470 4500
## [151] 4530 4560 4590 4620 4650 4680 4710 4740 4770 4800 4830 4860 4890 4920 4950
## [166] 4980 5010 5040 5070 5100 5130 5160 5190 5220 5250 5280 5310 5340 5370 5400
## [181] 5430 5460 5490 5520 5550 5580 5610 5640 5670 5700 5730 5760 5790 5820 5850
## [196] 5880 5910 5940 5970 6000 6030 6060 6090 6120 6150 6180 6210 6240 6270 6300
## [211] 6330 6360 6390 6420 6450 6480 6510 6540 6570 6600 6630 6660 6690 6720 6750
## [226] 6780 6810 6840 6870 6900 6930 6960 6990 7020 7050 7080 7110 7140 7170 7200
## [241] 7230 7260 7290 7320 7350 7380 7410 7440 7470
Repeat the list of intervals 1424 times (number of patients in Simmons)
start <- rep(start, times=1424)
end <- rep(end, times=1424)
intervals <- as.data.frame(cbind(start, end))
Add ID column to intervals
IDs <- rep(Simm$ID, each=249)
intervals <- as.data.frame(cbind(IDs, intervals))
intervals <- intervals %>% rename("ID"="IDs")
Join Simm and intervals
Simm <- left_join(intervals, Simm, by="ID")
Determine if event occurred during interval
Simm <- Simm %>% mutate(event=if_else((days_DeathTxCensor>=start & days_DeathTxCensor<=end), 1, 0))
Now will add date intervals for 5yr start and end times)
Simm <- Simm %>% mutate(end_5yr=(dx_date + days(end)))
Simm <- Simm %>% mutate(start_5yr=(end_5yr - days(1826)))
Now need to remove any rows where end_5yr is > date_DeathTxCensor (Amanda indicated need to remove rows where start_5yr > date_DeathTxCensor, but I don’t think this is correct because then we’d be assigning exposures to patients where they were alive/not transplanted for <5yrs for that exposure - need to double check with her)
Simm <- Simm %>% filter(!(end_5yr>DeathTxCensor_date & event!=1))
PM <- PM %>% mutate(start=as.IDate(PM_date))
PM <- PM %>% mutate(end=as.IDate(PM_date + months(1) - days(1)))
PM <- as.data.table(PM)
str(PM)
## Classes 'data.table' and 'data.frame': 323248 obs. of 5 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ PM_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 12.7 17.1 9.6 8.2 14.7 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
Creating a list of intervals we want to calculate exposures for:
Simm_intervals <- Simm %>% dplyr::select(ID, start_5yr, end_5yr)
Simm_intervals <- Simm_intervals %>% mutate_at(c("start_5yr", "end_5yr"), as.IDate)
Simm_intervals <- Simm_intervals %>% rename(c("start"="start_5yr", "end"="end_5yr"))
Simm_intervals <- as.data.table(Simm_intervals)
str(Simm_intervals)
## Classes 'data.table' and 'data.frame': 89507 obs. of 3 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ start: IDate, format: "1997-02-23" "1997-03-25" ...
## $ end : IDate, format: "2002-02-23" "2002-03-25" ...
## - attr(*, ".internal.selfref")=<externalptr>
PM_5yrWtedAvg <- intervalaverage(x=PM,
y=Simm_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
PM_5yrWtedAvgx <- PM_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
PM_5yrWtedAvgx <- PM_5yrWtedAvgx %>% rename("PM"="value", "start_5yr"="start", "end_5yr"="end")
PM_5yrWtedAvgx <- PM_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(PM_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 89507 obs. of 4 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ PM : num 12.5 12.4 12.3 12.3 12.5 ...
## $ start_5yr: Date, format: "1997-02-23" "1997-03-25" ...
## $ end_5yr : Date, format: "2002-02-23" "2002-03-25" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to Simm
Simm <- left_join(Simm, PM_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly SO4 level estimates by satellite at nearest lon/lat to Simmons patient residential addresses. These are linked to the patient ID.
outfile1 <- here("Simmons_fILD_2000_2017_SO4_2021_11_07.xlsx")
SO4 <- read_excel(outfile1)
SO4 <- SO4 %>% dplyr::select(!c(nrow, dist, lon, lat))
SO4 <- SO4 %>%
pivot_longer(cols=c(2:217), names_to="SO4_date", names_prefix="SO4_")
SO4x <- SO4
SO4x$SO4_date <- gsub("jan", "01-01-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("feb", "01-02-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("mar", "01-03-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("apr", "01-04-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("may", "01-05-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("jun", "01-06-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("jul", "01-07-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("aug", "01-08-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("sep", "01-09-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("oct", "01-10-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("nov", "01-11-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("dec", "01-12-20", SO4x$SO4_date)
SO4x$SO4_date <- format(as.Date(SO4x$SO4_date, format="%d-%m-%Y"),"%Y-%m-%d")
SO4x$SO4_date <- as.Date(SO4x$SO4_date)
SO4 <- SO4x
rm(SO4x)
SO4 <- SO4 %>% mutate(start=as.IDate(SO4_date))
SO4 <- SO4 %>% mutate(end=as.IDate(SO4_date + months(1) - days(1)))
SO4 <- as.data.table(SO4)
str(SO4)
## Classes 'data.table' and 'data.frame': 307584 obs. of 5 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ SO4_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 2.6 4.3 2.9 2.6 6.1 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
SO4_5yrWtedAvg <- intervalaverage(x=SO4,
y=Simm_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
SO4_5yrWtedAvgx <- SO4_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
SO4_5yrWtedAvgx <- SO4_5yrWtedAvgx %>% rename("SO4"="value", "start_5yr"="start", "end_5yr"="end")
SO4_5yrWtedAvgx <- SO4_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(SO4_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 89507 obs. of 4 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ SO4 : num 4.41 4.37 4.35 4.33 4.41 ...
## $ start_5yr: Date, format: "1997-02-23" "1997-03-25" ...
## $ end_5yr : Date, format: "2002-02-23" "2002-03-25" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to Simm
Simm <- left_join(Simm, SO4_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly NO3 level estimates by satellite at nearest lon/lat to Simmons patient residential addresses. These are linked to the patient ID.
outfile1 <- here("Simm_fILD_2000_2017_NO3_2021_11_05.xlsx")
NO3 <- read_excel(outfile1)
NO3 <- NO3 %>% dplyr::select(!c(nrow, dist, lon, lat))
NO3 <- NO3 %>%
pivot_longer(cols=c(2:217), names_to="NO3_date", names_prefix="NIT_")
NO3x <- NO3
NO3x$NO3_date <- gsub("jan", "01-01-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("feb", "01-02-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("mar", "01-03-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("apr", "01-04-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("may", "01-05-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("jun", "01-06-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("jul", "01-07-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("aug", "01-08-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("sep", "01-09-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("oct", "01-10-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("nov", "01-11-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("dec", "01-12-20", NO3x$NO3_date)
NO3x$NO3_date <- format(as.Date(NO3x$NO3_date, format="%d-%m-%Y"),"%Y-%m-%d")
NO3x$NO3_date <- as.Date(NO3x$NO3_date)
NO3 <- NO3x
rm(NO3x)
NO3 <- NO3 %>% mutate(start=as.IDate(NO3_date))
NO3 <- NO3 %>% mutate(end=as.IDate(NO3_date + months(1) - days(1)))
NO3 <- as.data.table(NO3)
str(NO3)
## Classes 'data.table' and 'data.frame': 307584 obs. of 5 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ NO3_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 2.2 3.3 1.3 0.8 1.1 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
NO3_5yrWtedAvg <- intervalaverage(x=NO3,
y=Simm_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
NO3_5yrWtedAvgx <- NO3_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
NO3_5yrWtedAvgx <- NO3_5yrWtedAvgx %>% rename("NO3"="value", "start_5yr"="start", "end_5yr"="end")
NO3_5yrWtedAvgx <- NO3_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(NO3_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 89507 obs. of 4 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ NO3 : num 1.4 1.41 1.4 1.38 1.36 ...
## $ start_5yr: Date, format: "1997-02-23" "1997-03-25" ...
## $ end_5yr : Date, format: "2002-02-23" "2002-03-25" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to Simm
Simm <- left_join(Simm, NO3_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly NH4 level estimates by satellite at nearest lon/lat to Simmons patient residential addresses. These are linked to the patient ID.
outfile1 <- here("Simm_fILD_2000_2017_NH4_2021_11_05.xlsx")
NH4 <- read_excel(outfile1)
NH4 <- NH4 %>% dplyr::select(!c(nrow, dist, lon, lat))
NH4 <- NH4 %>%
pivot_longer(cols=c(2:217), names_to="NH4_date", names_prefix="NH4_")
NH4x <- NH4
NH4x$NH4_date <- gsub("jan", "01-01-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("feb", "01-02-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("mar", "01-03-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("apr", "01-04-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("may", "01-05-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("jun", "01-06-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("jul", "01-07-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("aug", "01-08-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("sep", "01-09-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("oct", "01-10-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("nov", "01-11-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("dec", "01-12-20", NH4x$NH4_date)
NH4x$NH4_date <- format(as.Date(NH4x$NH4_date, format="%d-%m-%Y"),"%Y-%m-%d")
NH4x$NH4_date <- as.Date(NH4x$NH4_date)
NH4 <- NH4x
rm(NH4x)
NH4 <- NH4 %>% mutate(start=as.IDate(NH4_date))
NH4 <- NH4 %>% mutate(end=as.IDate(NH4_date + months(1) - days(1)))
NH4 <- as.data.table(NH4)
str(NH4)
## Classes 'data.table' and 'data.frame': 307584 obs. of 5 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ NH4_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 1.5 2.7 1.1 1.2 2.5 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
NH4_5yrWtedAvg <- intervalaverage(x=NH4,
y=Simm_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
NH4_5yrWtedAvgx <- NH4_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
NH4_5yrWtedAvgx <- NH4_5yrWtedAvgx %>% rename("NH4"="value", "start_5yr"="start", "end_5yr"="end")
NH4_5yrWtedAvgx <- NH4_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(NH4_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 89507 obs. of 4 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ NH4 : num 1.7 1.69 1.68 1.67 1.69 ...
## $ start_5yr: Date, format: "1997-02-23" "1997-03-25" ...
## $ end_5yr : Date, format: "2002-02-23" "2002-03-25" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to Simm
Simm <- left_join(Simm, NH4_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly BC level estimates by satellite at nearest lon/lat to Simmons patient residential addresses. These are linked to the patient ID.
outfile1 <- here("Simm_fILD_2000_2017_BC_2021_11_05.xlsx")
BC <- read_excel(outfile1)
BC <- BC %>% dplyr::select(!c(nrow, dist, lon, lat))
BC <- BC %>%
pivot_longer(cols=c(2:217), names_to="BC_date", names_prefix="BC_")
BCx <- BC
BCx$BC_date <- gsub("jan", "01-01-20", BCx$BC_date)
BCx$BC_date <- gsub("feb", "01-02-20", BCx$BC_date)
BCx$BC_date <- gsub("mar", "01-03-20", BCx$BC_date)
BCx$BC_date <- gsub("apr", "01-04-20", BCx$BC_date)
BCx$BC_date <- gsub("may", "01-05-20", BCx$BC_date)
BCx$BC_date <- gsub("jun", "01-06-20", BCx$BC_date)
BCx$BC_date <- gsub("jul", "01-07-20", BCx$BC_date)
BCx$BC_date <- gsub("aug", "01-08-20", BCx$BC_date)
BCx$BC_date <- gsub("sep", "01-09-20", BCx$BC_date)
BCx$BC_date <- gsub("oct", "01-10-20", BCx$BC_date)
BCx$BC_date <- gsub("nov", "01-11-20", BCx$BC_date)
BCx$BC_date <- gsub("dec", "01-12-20", BCx$BC_date)
BCx$BC_date <- format(as.Date(BCx$BC_date, format="%d-%m-%Y"),"%Y-%m-%d")
BCx$BC_date <- as.Date(BCx$BC_date)
BC <- BCx
rm(BCx)
BC <- BC %>% mutate(start=as.IDate(BC_date))
BC <- BC %>% mutate(end=as.IDate(BC_date + months(1) - days(1)))
BC <- as.data.table(BC)
str(BC)
## Classes 'data.table' and 'data.frame': 307584 obs. of 5 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ BC_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 0.7 1 0.8 0.7 1.4 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
BC_5yrWtedAvg <- intervalaverage(x=BC,
y=Simm_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
BC_5yrWtedAvgx <- BC_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
BC_5yrWtedAvgx <- BC_5yrWtedAvgx %>% rename("BC"="value", "start_5yr"="start", "end_5yr"="end")
BC_5yrWtedAvgx <- BC_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(BC_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 89507 obs. of 4 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ BC : num 0.774 0.768 0.762 0.753 0.763 ...
## $ start_5yr: Date, format: "1997-02-23" "1997-03-25" ...
## $ end_5yr : Date, format: "2002-02-23" "2002-03-25" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to Simm
Simm <- left_join(Simm, BC_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly OM level estimates by satellite at nearest lon/lat to Simmons patient residential addresses. These are linked to the patient ID.
outfile1 <- here("Simm_fILD_2000_2017_OM_2021_11_05.xlsx")
OM <- read_excel(outfile1)
OM <- OM %>% dplyr::select(!c(nrow, dist, lon, lat))
OM <- OM %>%
pivot_longer(cols=c(2:217), names_to="OM_date", names_prefix="OM_")
OMx <- OM
OMx$OM_date <- gsub("jan", "01-01-20", OMx$OM_date)
OMx$OM_date <- gsub("feb", "01-02-20", OMx$OM_date)
OMx$OM_date <- gsub("mar", "01-03-20", OMx$OM_date)
OMx$OM_date <- gsub("apr", "01-04-20", OMx$OM_date)
OMx$OM_date <- gsub("may", "01-05-20", OMx$OM_date)
OMx$OM_date <- gsub("jun", "01-06-20", OMx$OM_date)
OMx$OM_date <- gsub("jul", "01-07-20", OMx$OM_date)
OMx$OM_date <- gsub("aug", "01-08-20", OMx$OM_date)
OMx$OM_date <- gsub("sep", "01-09-20", OMx$OM_date)
OMx$OM_date <- gsub("oct", "01-10-20", OMx$OM_date)
OMx$OM_date <- gsub("nov", "01-11-20", OMx$OM_date)
OMx$OM_date <- gsub("dec", "01-12-20", OMx$OM_date)
OMx$OM_date <- format(as.Date(OMx$OM_date, format="%d-%m-%Y"),"%Y-%m-%d")
OMx$OM_date <- as.Date(OMx$OM_date)
OM <- OMx
rm(OMx)
OM <- OM %>% mutate(start=as.IDate(OM_date))
OM <- OM %>% mutate(end=as.IDate(OM_date + months(1) - days(1)))
OM <- as.data.table(OM)
str(OM)
## Classes 'data.table' and 'data.frame': 307584 obs. of 5 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ OM_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 3 3.2 3.3 2 3.7 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
OM_5yrWtedAvg <- intervalaverage(x=OM,
y=Simm_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
OM_5yrWtedAvgx <- OM_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
OM_5yrWtedAvgx <- OM_5yrWtedAvgx %>% rename("OM"="value", "start_5yr"="start", "end_5yr"="end")
OM_5yrWtedAvgx <- OM_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(OM_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 89507 obs. of 4 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ OM : num 2.9 2.89 2.88 2.85 2.9 ...
## $ start_5yr: Date, format: "1997-02-23" "1997-03-25" ...
## $ end_5yr : Date, format: "2002-02-23" "2002-03-25" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to Simm
Simm <- left_join(Simm, OM_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly SS level estimates by satellite at nearest lon/lat to Simmons patient residential addresses. These are linked to the patient ID.
outfile1 <- here("Simm_fILD_2000_2017_SS_2021_11_05.xlsx")
SS <- read_excel(outfile1)
SS <- SS %>% dplyr::select(!c(nrow, dist, lon, lat))
SS <- SS %>%
pivot_longer(cols=c(2:217), names_to="SS_date", names_prefix="SS_")
SSx <- SS
SSx$SS_date <- gsub("jan", "01-01-20", SSx$SS_date)
SSx$SS_date <- gsub("feb", "01-02-20", SSx$SS_date)
SSx$SS_date <- gsub("mar", "01-03-20", SSx$SS_date)
SSx$SS_date <- gsub("apr", "01-04-20", SSx$SS_date)
SSx$SS_date <- gsub("may", "01-05-20", SSx$SS_date)
SSx$SS_date <- gsub("jun", "01-06-20", SSx$SS_date)
SSx$SS_date <- gsub("jul", "01-07-20", SSx$SS_date)
SSx$SS_date <- gsub("aug", "01-08-20", SSx$SS_date)
SSx$SS_date <- gsub("sep", "01-09-20", SSx$SS_date)
SSx$SS_date <- gsub("oct", "01-10-20", SSx$SS_date)
SSx$SS_date <- gsub("nov", "01-11-20", SSx$SS_date)
SSx$SS_date <- gsub("dec", "01-12-20", SSx$SS_date)
SSx$SS_date <- format(as.Date(SSx$SS_date, format="%d-%m-%Y"),"%Y-%m-%d")
SSx$SS_date <- as.Date(SSx$SS_date)
SS <- SSx
rm(SSx)
SS <- SS %>% mutate(start=as.IDate(SS_date))
SS <- SS %>% mutate(end=as.IDate(SS_date + months(1) - days(1)))
SS <- as.data.table(SS)
str(SS)
## Classes 'data.table' and 'data.frame': 307584 obs. of 5 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ SS_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 0 0.8 0.5 0 0 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
SS_5yrWtedAvg <- intervalaverage(x=SS,
y=Simm_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
SS_5yrWtedAvgx <- SS_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
SS_5yrWtedAvgx <- SS_5yrWtedAvgx %>% rename("SS"="value", "start_5yr"="start", "end_5yr"="end")
SS_5yrWtedAvgx <- SS_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(SS_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 89507 obs. of 4 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ SS : num 0.241 0.248 0.242 0.248 0.249 ...
## $ start_5yr: Date, format: "1997-02-23" "1997-03-25" ...
## $ end_5yr : Date, format: "2002-02-23" "2002-03-25" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to Simm
Simm <- left_join(Simm, SS_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly Soil level estimates by satellite at nearest lon/lat to Simmons patient residential addresses. These are linked to the patient ID.
outfile1 <- here("Simm_fILD_2000_2017_Soil_2021_11_05.xlsx")
Soil <- read_excel(outfile1)
Soil <- Soil %>% dplyr::select(!c(nrow, dist, lon, lat))
Soil <- Soil %>%
pivot_longer(cols=c(2:217), names_to="Soil_date", names_prefix="soil_")
Soilx <- Soil
Soilx$Soil_date <- gsub("jan", "01-01-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("feb", "01-02-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("mar", "01-03-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("apr", "01-04-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("may", "01-05-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("jun", "01-06-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("jul", "01-07-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("aug", "01-08-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("sep", "01-09-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("oct", "01-10-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("nov", "01-11-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("dec", "01-12-20", Soilx$Soil_date)
Soilx$Soil_date <- format(as.Date(Soilx$Soil_date, format="%d-%m-%Y"),"%Y-%m-%d")
Soilx$Soil_date <- as.Date(Soilx$Soil_date)
Soil <- Soilx
rm(Soilx)
Soil <- Soil %>% mutate(start=as.IDate(Soil_date))
Soil <- Soil %>% mutate(end=as.IDate(Soil_date + months(1) - days(1)))
Soil <- as.data.table(Soil)
str(Soil)
## Classes 'data.table' and 'data.frame': 307584 obs. of 5 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ Soil_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 0.4 0.8 0.7 0.3 0.7 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
Soil_5yrWtedAvg <- intervalaverage(x=Soil,
y=Simm_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
Soil_5yrWtedAvgx <- Soil_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
Soil_5yrWtedAvgx <- Soil_5yrWtedAvgx %>% rename("Soil"="value", "start_5yr"="start", "end_5yr"="end")
Soil_5yrWtedAvgx <- Soil_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(Soil_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 89507 obs. of 4 variables:
## $ ID : num 1097 1097 1097 1097 1097 ...
## $ Soil : num 0.521 0.513 0.511 0.511 0.515 ...
## $ start_5yr: Date, format: "1997-02-23" "1997-03-25" ...
## $ end_5yr : Date, format: "2002-02-23" "2002-03-25" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to Simm
Simm <- left_join(Simm, Soil_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly PM level estimates by satellite at nearest lon/lat to PFF patient residential addresses. These are linked to the patient ID.
outfile1 <- here("PFF_fILD_2000_2018_PM25_2021_10_08.xlsx")
PM <- read_excel(outfile1)
PM <- PM %>% rename("SSID"="ID")
Match up PM ID to SSID from matching file
outfile2 <- here("PFF_fILD_PM25_BaselineData_2021_10_20.xlsx")
PM25 <- read_excel(outfile2)
PM25 <- PM25 %>% dplyr::select(ID, SSID)
Merge PM and PM
PM <- left_join(PM, PM25, by="SSID")
Here I am importing the file containing the complete baseline clinical and demographic data for 1905 PFF patients
outfile3 <- here("PFF_fILDPts_BaselineData_ConsentDateReference_2022_08_01.xlsx")
PFF <- read_excel(outfile3)
I used a inner_join here so that the complete “PFF” dataframe only includes patients with fILD that have all baseline demographics and PM data.
PFF <- inner_join(PFF, PM, by="SSID")
This results in 1905 complete records
Reorder so “ID” is the first column
PFF <- PFF %>% dplyr::select(ID, everything(.))
PFF <- PFF %>%
mutate(dx_yr = format(as.Date(PFF$dx_date, format="%Y-%m-%d"),"%Y"))
PFF$dx_yr <- as.numeric(PFF$dx_yr)
PFF$site <- substr(PFF$SSID, 1,3)
PFF$site <- as.factor(PFF$site)
str(PFF$site)
## Factor w/ 42 levels "01R","02R","03R",..: 24 2 3 3 3 3 3 4 4 4 ...
Removing Pittsburgh site
PFF <- PFF %>% filter(!site=="08R")
Reorder so “ID” is the first column
PM <- PM %>% dplyr::select(ID, everything(.))
PM <- PM %>% dplyr::select(!SSID)
PM <- PM %>% dplyr::select(!c(nrow, dist, lon, lat))
colnames(PM)
## [1] "ID" "PM25_jan00" "PM25_feb00" "PM25_mar00" "PM25_apr00"
## [6] "PM25_may00" "PM25_jun00" "PM25_jul00" "PM25_aug00" "PM25_sep00"
## [11] "PM25_oct00" "PM25_nov00" "PM25_dec00" "PM25_jan01" "PM25_feb01"
## [16] "PM25_mar01" "PM25_apr01" "PM25_may01" "PM25_jun01" "PM25_jul01"
## [21] "PM25_aug01" "PM25_sep01" "PM25_oct01" "PM25_nov01" "PM25_dec01"
## [26] "PM25_jan02" "PM25_feb02" "PM25_mar02" "PM25_apr02" "PM25_may02"
## [31] "PM25_jun02" "PM25_jul02" "PM25_aug02" "PM25_sep02" "PM25_oct02"
## [36] "PM25_nov02" "PM25_dec02" "PM25_jan03" "PM25_feb03" "PM25_mar03"
## [41] "PM25_apr03" "PM25_may03" "PM25_jun03" "PM25_jul03" "PM25_aug03"
## [46] "PM25_sep03" "PM25_oct03" "PM25_nov03" "PM25_dec03" "PM25_jan04"
## [51] "PM25_feb04" "PM25_mar04" "PM25_apr04" "PM25_may04" "PM25_jun04"
## [56] "PM25_jul04" "PM25_aug04" "PM25_sep04" "PM25_oct04" "PM25_nov04"
## [61] "PM25_dec04" "PM25_jan05" "PM25_feb05" "PM25_mar05" "PM25_apr05"
## [66] "PM25_may05" "PM25_jun05" "PM25_jul05" "PM25_aug05" "PM25_sep05"
## [71] "PM25_oct05" "PM25_nov05" "PM25_dec05" "PM25_jan06" "PM25_feb06"
## [76] "PM25_mar06" "PM25_apr06" "PM25_may06" "PM25_jun06" "PM25_jul06"
## [81] "PM25_aug06" "PM25_sep06" "PM25_oct06" "PM25_nov06" "PM25_dec06"
## [86] "PM25_jan07" "PM25_feb07" "PM25_mar07" "PM25_apr07" "PM25_may07"
## [91] "PM25_jun07" "PM25_jul07" "PM25_aug07" "PM25_sep07" "PM25_oct07"
## [96] "PM25_nov07" "PM25_dec07" "PM25_jan08" "PM25_feb08" "PM25_mar08"
## [101] "PM25_apr08" "PM25_may08" "PM25_jun08" "PM25_jul08" "PM25_aug08"
## [106] "PM25_sep08" "PM25_oct08" "PM25_nov08" "PM25_dec08" "PM25_jan09"
## [111] "PM25_feb09" "PM25_mar09" "PM25_apr09" "PM25_may09" "PM25_jun09"
## [116] "PM25_jul09" "PM25_aug09" "PM25_sep09" "PM25_oct09" "PM25_nov09"
## [121] "PM25_dec09" "PM25_jan10" "PM25_feb10" "PM25_mar10" "PM25_apr10"
## [126] "PM25_may10" "PM25_jun10" "PM25_jul10" "PM25_aug10" "PM25_sep10"
## [131] "PM25_oct10" "PM25_nov10" "PM25_dec10" "PM25_jan11" "PM25_feb11"
## [136] "PM25_mar11" "PM25_apr11" "PM25_may11" "PM25_jun11" "PM25_jul11"
## [141] "PM25_aug11" "PM25_sep11" "PM25_oct11" "PM25_nov11" "PM25_dec11"
## [146] "PM25_jan12" "PM25_feb12" "PM25_mar12" "PM25_apr12" "PM25_may12"
## [151] "PM25_jun12" "PM25_jul12" "PM25_aug12" "PM25_sep12" "PM25_oct12"
## [156] "PM25_nov12" "PM25_dec12" "PM25_jan13" "PM25_feb13" "PM25_mar13"
## [161] "PM25_apr13" "PM25_may13" "PM25_jun13" "PM25_jul13" "PM25_aug13"
## [166] "PM25_sep13" "PM25_oct13" "PM25_nov13" "PM25_dec13" "PM25_jan14"
## [171] "PM25_feb14" "PM25_mar14" "PM25_apr14" "PM25_may14" "PM25_jun14"
## [176] "PM25_jul14" "PM25_aug14" "PM25_sep14" "PM25_oct14" "PM25_nov14"
## [181] "PM25_dec14" "PM25_jan15" "PM25_feb15" "PM25_mar15" "PM25_apr15"
## [186] "PM25_may15" "PM25_jun15" "PM25_jul15" "PM25_aug15" "PM25_sep15"
## [191] "PM25_oct15" "PM25_nov15" "PM25_dec15" "PM25_jan16" "PM25_feb16"
## [196] "PM25_mar16" "PM25_apr16" "PM25_may16" "PM25_jun16" "PM25_jul16"
## [201] "PM25_aug16" "PM25_sep16" "PM25_oct16" "PM25_nov16" "PM25_dec16"
## [206] "PM25_jan17" "PM25_feb17" "PM25_mar17" "PM25_apr17" "PM25_may17"
## [211] "PM25_jun17" "PM25_jul17" "PM25_aug17" "PM25_sep17" "PM25_oct17"
## [216] "PM25_nov17" "PM25_dec17" "PM25_jan18" "PM25_feb18" "PM25_mar18"
## [221] "PM25_apr18" "PM25_may18" "PM25_jun18" "PM25_jul18" "PM25_aug18"
## [226] "PM25_sep18" "PM25_oct18" "PM25_nov18" "PM25_dec18"
PM <- PM %>%
pivot_longer(cols=c(2:228), names_to="PM_date", names_prefix="PM25_")
PMx <- PM
PMx$PM_date <- gsub("jan", "01-01-20", PMx$PM_date)
PMx$PM_date <- gsub("feb", "01-02-20", PMx$PM_date)
PMx$PM_date <- gsub("mar", "01-03-20", PMx$PM_date)
PMx$PM_date <- gsub("apr", "01-04-20", PMx$PM_date)
PMx$PM_date <- gsub("may", "01-05-20", PMx$PM_date)
PMx$PM_date <- gsub("jun", "01-06-20", PMx$PM_date)
PMx$PM_date <- gsub("jul", "01-07-20", PMx$PM_date)
PMx$PM_date <- gsub("aug", "01-08-20", PMx$PM_date)
PMx$PM_date <- gsub("sep", "01-09-20", PMx$PM_date)
PMx$PM_date <- gsub("oct", "01-10-20", PMx$PM_date)
PMx$PM_date <- gsub("nov", "01-11-20", PMx$PM_date)
PMx$PM_date <- gsub("dec", "01-12-20", PMx$PM_date)
PMx$PM_date <- format(as.Date(PMx$PM_date, format="%d-%m-%Y"),"%Y-%m-%d")
PMx$PM_date <- as.Date(PMx$PM_date)
PM <- PMx
rm(PMx)
Extracting year of diagnosis and year of death/transplant/censoring
#Start with the year of diagnosis
PFF <- PFF %>%
mutate(dx_yrmo = format(as.Date(PFF$dx_date, format="%Y-%m-%d"),"%Y-%m"))
PFF <- PFF %>%
mutate(dx_yr = format(as.Date(PFF$dx_date, format="%Y-%m-%d"),"%Y"))
PFF$dx_yr <- as.numeric(PFF$dx_yr)
#Then the year of death or lung transplant
PFF <- PFF %>%
mutate(deathORtx_date = if_else(!is.na(tx_date), tx_date, death_date))
PFF <- PFF %>%
mutate(deathORtx_yrmo = format(as.Date(PFF$deathORtx_date, format="%Y-%m-%d"),"%Y-%m"))
#Then the year the records were last updated (i.e. year of censoring)
PFF <- PFF %>%
mutate(DeathTxCensor_date = if_else(!is.na(deathORtx_date), deathORtx_date, censor_date))
PFF <- PFF %>%
mutate(censor_yrmo = format(as.Date(PFF$DeathTxCensor_date, format="%Y-%m-%d"),"%Y-%m"))
PFF <- PFF %>% dplyr::select(c(ID, site, age_dx, sex, smokeHx, race, dich_Race, pct_belowpoverty, dx_group, dx, dx_date, death_date, tx_date, DeathTxCensor_date, censor_date, fvc_date, dlco_date, fvc_pct, dlco_pct, status, deadORtx, time_DeathTxCensor, dx_yr))
PFF <- PFF %>%
mutate_at(c("death_date", "censor_date", "tx_date", "dx_date", "fvc_date", "dlco_date", "DeathTxCensor_date"), as.Date)
str(PFF)
## tibble [1,870 × 23] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:1870] 1 2 3 4 5 6 7 8 9 10 ...
## $ site : Factor w/ 42 levels "01R","02R","03R",..: 24 2 3 3 3 3 3 4 4 4 ...
## $ age_dx : num [1:1870] 64.5 42.7 49.6 70.6 50.6 ...
## $ sex : chr [1:1870] "Female" "Female" "Female" "Female" ...
## $ smokeHx : chr [1:1870] "Never" "Never" "Never" "Never" ...
## $ race : chr [1:1870] "W" "W" "W" "W" ...
## $ dich_Race : chr [1:1870] "White" "White" "White" "White" ...
## $ pct_belowpoverty : num [1:1870] 3.4 7.7 9.3 8.4 9.1 13.7 4.9 6.5 5.9 10.6 ...
## $ dx_group : chr [1:1870] "CTD-ILD" "CTD-ILD" "CTD-ILD" "CTD-ILD" ...
## $ dx : chr [1:1870] "ANKSPON_ILD" "ANTISYNTHETASE" "ANTISYNTHETASE" "ANTISYNTHETASE" ...
## $ dx_date : Date[1:1870], format: "2006-07-07" "2017-06-16" ...
## $ death_date : Date[1:1870], format: NA NA ...
## $ tx_date : Date[1:1870], format: NA NA ...
## $ DeathTxCensor_date: Date[1:1870], format: "2021-03-03" "2019-04-01" ...
## $ censor_date : Date[1:1870], format: "2021-03-03" "2019-04-01" ...
## $ fvc_date : Date[1:1870], format: "2016-09-23" "2017-08-25" ...
## $ dlco_date : Date[1:1870], format: "2016-09-23" "2017-08-25" ...
## $ fvc_pct : num [1:1870] 38.1 72.9 85 79.4 86.1 ...
## $ dlco_pct : num [1:1870] 80.4 80.8 105.2 41.2 82.3 ...
## $ status : chr [1:1870] "0" "0" "0" "0" ...
## $ deadORtx : num [1:1870] 0 0 0 0 0 0 0 0 0 0 ...
## $ time_DeathTxCensor: num [1:1870] 4.44 1.8 3.95 2.64 2.64 ...
## $ dx_yr : num [1:1870] 2006 2017 2016 2017 2017 ...
Need to correct smoking variables
PFF$smokeHx <- as.character(PFF$smokeHx)
PFF <- PFF %>% mutate(smokeHx1=if_else(is.na(smokeHx), "Unknown", smokeHx))
Now need to remove old smoking variables and rename new ones
PFF <- PFF %>% dplyr::select(-c(smokeHx))
PFF <- PFF %>% rename(c("smokeHx"="smokeHx1"))
PFF$smokeHx <- as.factor(PFF$smokeHx)
PFF$smokeHx <- fct_relevel(PFF$smokeHx, c("Never","Ever","Unknown"))
## Warning: 1 unknown level in `f`: Unknown
Need to correct other factor variables
PFF$sex <- fct_relevel(PFF$sex, c("M","F"))
## Warning: 2 unknown levels in `f`: M and F
PFF$race <- fct_relevel(PFF$race, c("W","B","A","N","U"))
## Warning: 1 unknown level in `f`: N
PFF$dich_Race <- fct_relevel(PFF$dich_Race, c("White","Non-White"))
PFF$dx <- fct_relevel(PFF$dx, c("IPF"))
PFF$dx_group <- fct_relevel(PFF$dx_group, c("IPF"))
PFF <- PFF %>% mutate_at(c("status", "site"), as.factor)
PFF <- PFF %>% mutate(dx_IPF=ifelse(dx=="IPF", "IPF", "not_IPF"))
PFF$dx_IPF <- fct_relevel(PFF$dx_IPF, c("IPF"))
Creating this empirical cumulative distribution will allow us to combine the analyses of all three cohorts even though the measurements for disadvantage are different between the three.
plot(ecdf(PFF$pct_belowpoverty))
PFF$disadv <- ecdf(PFF$pct_belowpoverty)(PFF$pct_belowpoverty)
Take down PFF dataset to the 1905 patients with complete data
IDs <- as.data.table(unique(PM$ID))
IDs <- IDs %>% rename("ID"="V1")
PFF <- left_join(IDs, PFF, by="ID")
PFF <- PFF %>% mutate(days_DeathTxCensor=(time_DeathTxCensor*365.25))
Longest time_DeathTxCensor= 1848days
30-day longest time interval would be 1860 days
start <- seq(1, 1831, by = 30)
start
## [1] 1 31 61 91 121 151 181 211 241 271 301 331 361 391 421
## [16] 451 481 511 541 571 601 631 661 691 721 751 781 811 841 871
## [31] 901 931 961 991 1021 1051 1081 1111 1141 1171 1201 1231 1261 1291 1321
## [46] 1351 1381 1411 1441 1471 1501 1531 1561 1591 1621 1651 1681 1711 1741 1771
## [61] 1801 1831
end <- seq(30, 1860, by = 30)
end
## [1] 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450
## [16] 480 510 540 570 600 630 660 690 720 750 780 810 840 870 900
## [31] 930 960 990 1020 1050 1080 1110 1140 1170 1200 1230 1260 1290 1320 1350
## [46] 1380 1410 1440 1470 1500 1530 1560 1590 1620 1650 1680 1710 1740 1770 1800
## [61] 1830 1860
Repeat the list of intervals 1905 times (number of patients in PFF)
start <- rep(start, times=1905)
end <- rep(end, times=1905)
intervals <- as.data.frame(cbind(start, end))
Add ID column to intervals
IDs <- rep(PFF$ID, each=62)
intervals <- as.data.frame(cbind(IDs, intervals))
intervals <- intervals %>% rename("ID"="IDs")
Join PFF and intervals
PFF <- left_join(intervals, PFF, by="ID")
Determine if event occurred during interval
PFF <- PFF %>% mutate(event=if_else((days_DeathTxCensor>=start & days_DeathTxCensor<=end), 1, 0))
Now will add date intervals for 5yr start and end times)
PFF <- PFF %>% mutate(end_5yr=(dx_date + days(end)))
PFF <- PFF %>% mutate(start_5yr=(end_5yr - days(1826)))
Now need to remove any rows where end_5yr is > date_DeathTxCensor (Amanda indicated need to remove rows where start_5yr > date_DeathTxCensor, but I don’t think this is correct because then we’d be assigning exposures to patients where they were alive/not transplanted for <5yrs for that exposure - need to double check with her)
PFF <- PFF %>% filter(!(end_5yr>DeathTxCensor_date & event!=1))
PM <- PM %>% mutate(start=as.IDate(PM_date))
PM <- PM %>% mutate(end=as.IDate(PM_date + months(1) - days(1)))
PM <- as.data.table(PM)
str(PM)
## Classes 'data.table' and 'data.frame': 432435 obs. of 6 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ PM25_dec18: num 11.1 11.1 11.1 11.1 11.1 ...
## $ PM_date : Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 14.3 12.7 11.9 7.6 7.4 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
Creating a list of intervals we want to calculate exposures for:
PFF_intervals <- PFF %>% dplyr::select(ID, start_5yr, end_5yr)
PFF_intervals <- PFF_intervals %>% mutate_at(c("start_5yr", "end_5yr"), as.IDate)
PFF_intervals <- PFF_intervals %>% rename(c("start"="start_5yr", "end"="end_5yr"))
PFF_intervals <- as.data.table(PFF_intervals)
str(PFF_intervals)
## Classes 'data.table' and 'data.frame': 87626 obs. of 3 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ start: IDate, format: "2001-08-06" "2001-09-05" ...
## $ end : IDate, format: "2006-08-06" "2006-09-05" ...
## - attr(*, ".internal.selfref")=<externalptr>
PM_5yrWtedAvg <- intervalaverage(x=PM,
y=PFF_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
PM_5yrWtedAvgx <- PM_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
PM_5yrWtedAvgx <- PM_5yrWtedAvgx %>% rename("PM"="value", "start_5yr"="start", "end_5yr"="end")
PM_5yrWtedAvgx <- PM_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(PM_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 87626 obs. of 4 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ PM : num 9.9 9.89 9.88 9.89 9.85 ...
## $ start_5yr: Date, format: "2001-08-06" "2001-09-05" ...
## $ end_5yr : Date, format: "2006-08-06" "2006-09-05" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to PFF
PFF <- left_join(PFF, PM_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly SO4 level estimates by satellite at nearest lon/lat to PFF patient residential addresses. These are linked to the patient ID.
outfile1 <- here("PFF_fILD_2000_2017_SO4_2021_11_05.xlsx")
SO4 <- read_excel(outfile1)
SO4 <- SO4 %>% dplyr::select(!c(nrow, dist, lon, lat))
SO4 <- SO4 %>%
pivot_longer(cols=c(2:217), names_to="SO4_date", names_prefix="SO4_")
SO4x <- SO4
SO4x$SO4_date <- gsub("jan", "01-01-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("feb", "01-02-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("mar", "01-03-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("apr", "01-04-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("may", "01-05-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("jun", "01-06-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("jul", "01-07-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("aug", "01-08-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("sep", "01-09-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("oct", "01-10-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("nov", "01-11-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("dec", "01-12-20", SO4x$SO4_date)
SO4x$SO4_date <- format(as.Date(SO4x$SO4_date, format="%d-%m-%Y"),"%Y-%m-%d")
SO4x$SO4_date <- as.Date(SO4x$SO4_date)
SO4 <- SO4x
rm(SO4x)
SO4 <- SO4 %>% mutate(start=as.IDate(SO4_date))
SO4 <- SO4 %>% mutate(end=as.IDate(SO4_date + months(1) - days(1)))
SO4 <- as.data.table(SO4)
str(SO4)
## Classes 'data.table' and 'data.frame': 411480 obs. of 5 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ SO4_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 2.6 2.1 2.4 1.6 1.9 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
SO4_5yrWtedAvg <- intervalaverage(x=SO4,
y=PFF_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
SO4_5yrWtedAvgx <- SO4_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
SO4_5yrWtedAvgx <- SO4_5yrWtedAvgx %>% rename("SO4"="value", "start_5yr"="start", "end_5yr"="end")
SO4_5yrWtedAvgx <- SO4_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(SO4_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 87626 obs. of 4 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ SO4 : num 2.3 2.3 2.28 2.29 2.27 ...
## $ start_5yr: Date, format: "2001-08-06" "2001-09-05" ...
## $ end_5yr : Date, format: "2006-08-06" "2006-09-05" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to PFF
PFF <- left_join(PFF, SO4_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly NO3 level estimates by satellite at nearest lon/lat to PFF patient residential addresses. These are linked to the patient ID.
outfile1 <- here("PFF_fILD_2000_2017_NO3_2021_11_05.xlsx")
NO3 <- read_excel(outfile1)
NO3 <- NO3 %>% dplyr::select(!c(nrow, dist, lon, lat))
NO3 <- NO3 %>%
pivot_longer(cols=c(2:217), names_to="NO3_date", names_prefix="NIT_")
NO3x <- NO3
NO3x$NO3_date <- gsub("jan", "01-01-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("feb", "01-02-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("mar", "01-03-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("apr", "01-04-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("may", "01-05-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("jun", "01-06-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("jul", "01-07-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("aug", "01-08-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("sep", "01-09-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("oct", "01-10-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("nov", "01-11-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("dec", "01-12-20", NO3x$NO3_date)
NO3x$NO3_date <- format(as.Date(NO3x$NO3_date, format="%d-%m-%Y"),"%Y-%m-%d")
NO3x$NO3_date <- as.Date(NO3x$NO3_date)
NO3 <- NO3x
rm(NO3x)
NO3 <- NO3 %>% mutate(start=as.IDate(NO3_date))
NO3 <- NO3 %>% mutate(end=as.IDate(NO3_date + months(1) - days(1)))
NO3 <- as.data.table(NO3)
str(NO3)
## Classes 'data.table' and 'data.frame': 411480 obs. of 5 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ NO3_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 4 4.2 3.8 1.9 1.4 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
NO3_5yrWtedAvg <- intervalaverage(x=NO3,
y=PFF_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
NO3_5yrWtedAvgx <- NO3_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
NO3_5yrWtedAvgx <- NO3_5yrWtedAvgx %>% rename("NO3"="value", "start_5yr"="start", "end_5yr"="end")
NO3_5yrWtedAvgx <- NO3_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(NO3_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 87626 obs. of 4 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ NO3 : num 2.16 2.17 2.17 2.17 2.17 ...
## $ start_5yr: Date, format: "2001-08-06" "2001-09-05" ...
## $ end_5yr : Date, format: "2006-08-06" "2006-09-05" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to PFF
PFF <- left_join(PFF, NO3_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly NH4 level estimates by satellite at nearest lon/lat to PFF patient residential addresses. These are linked to the patient ID.
outfile1 <- here("PFF_fILD_2000_2017_NH4_2021_11_05.xlsx")
NH4 <- read_excel(outfile1)
NH4 <- NH4 %>% dplyr::select(!c(nrow, dist, lon, lat))
NH4 <- NH4 %>%
pivot_longer(cols=c(2:217), names_to="NH4_date", names_prefix="NH4_")
NH4x <- NH4
NH4x$NH4_date <- gsub("jan", "01-01-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("feb", "01-02-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("mar", "01-03-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("apr", "01-04-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("may", "01-05-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("jun", "01-06-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("jul", "01-07-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("aug", "01-08-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("sep", "01-09-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("oct", "01-10-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("nov", "01-11-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("dec", "01-12-20", NH4x$NH4_date)
NH4x$NH4_date <- format(as.Date(NH4x$NH4_date, format="%d-%m-%Y"),"%Y-%m-%d")
NH4x$NH4_date <- as.Date(NH4x$NH4_date)
NH4 <- NH4x
rm(NH4x)
NH4 <- NH4 %>% mutate(start=as.IDate(NH4_date))
NH4 <- NH4 %>% mutate(end=as.IDate(NH4_date + months(1) - days(1)))
NH4 <- as.data.table(NH4)
str(NH4)
## Classes 'data.table' and 'data.frame': 411480 obs. of 5 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ NH4_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 1.7 2.1 1.3 1 0.9 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
NH4_5yrWtedAvg <- intervalaverage(x=NH4,
y=PFF_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
NH4_5yrWtedAvgx <- NH4_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
NH4_5yrWtedAvgx <- NH4_5yrWtedAvgx %>% rename("NH4"="value", "start_5yr"="start", "end_5yr"="end")
NH4_5yrWtedAvgx <- NH4_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(NH4_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 87626 obs. of 4 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ NH4 : num 1.25 1.25 1.25 1.26 1.27 ...
## $ start_5yr: Date, format: "2001-08-06" "2001-09-05" ...
## $ end_5yr : Date, format: "2006-08-06" "2006-09-05" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to PFF
PFF <- left_join(PFF, NH4_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly BC level estimates by satellite at nearest lon/lat to PFF patient residential addresses. These are linked to the patient ID.
outfile1 <- here("PFF_fILD_2000_2017_BC_2021_11_05.xlsx")
BC <- read_excel(outfile1)
BC <- BC %>% dplyr::select(!c(nrow, dist, lon, lat))
BC <- BC %>%
pivot_longer(cols=c(2:217), names_to="BC_date", names_prefix="BC_")
BCx <- BC
BCx$BC_date <- gsub("jan", "01-01-20", BCx$BC_date)
BCx$BC_date <- gsub("feb", "01-02-20", BCx$BC_date)
BCx$BC_date <- gsub("mar", "01-03-20", BCx$BC_date)
BCx$BC_date <- gsub("apr", "01-04-20", BCx$BC_date)
BCx$BC_date <- gsub("may", "01-05-20", BCx$BC_date)
BCx$BC_date <- gsub("jun", "01-06-20", BCx$BC_date)
BCx$BC_date <- gsub("jul", "01-07-20", BCx$BC_date)
BCx$BC_date <- gsub("aug", "01-08-20", BCx$BC_date)
BCx$BC_date <- gsub("sep", "01-09-20", BCx$BC_date)
BCx$BC_date <- gsub("oct", "01-10-20", BCx$BC_date)
BCx$BC_date <- gsub("nov", "01-11-20", BCx$BC_date)
BCx$BC_date <- gsub("dec", "01-12-20", BCx$BC_date)
BCx$BC_date <- format(as.Date(BCx$BC_date, format="%d-%m-%Y"),"%Y-%m-%d")
BCx$BC_date <- as.Date(BCx$BC_date)
BC <- BCx
rm(BCx)
BC <- BC %>% mutate(start=as.IDate(BC_date))
BC <- BC %>% mutate(end=as.IDate(BC_date + months(1) - days(1)))
BC <- as.data.table(BC)
str(BC)
## Classes 'data.table' and 'data.frame': 411480 obs. of 5 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BC_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 0.7 0.5 0.7 0.5 0.5 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
BC_5yrWtedAvg <- intervalaverage(x=BC,
y=PFF_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
BC_5yrWtedAvgx <- BC_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
BC_5yrWtedAvgx <- BC_5yrWtedAvgx %>% rename("BC"="value", "start_5yr"="start", "end_5yr"="end")
BC_5yrWtedAvgx <- BC_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(BC_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 87626 obs. of 4 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BC : num 0.568 0.568 0.57 0.575 0.574 ...
## $ start_5yr: Date, format: "2001-08-06" "2001-09-05" ...
## $ end_5yr : Date, format: "2006-08-06" "2006-09-05" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to PFF
PFF <- left_join(PFF, BC_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly OM level estimates by satellite at nearest lon/lat to PFF patient residential addresses. These are linked to the patient ID.
outfile1 <- here("PFF_fILD_2000_2017_OM_2021_11_05.xlsx")
OM <- read_excel(outfile1)
OM <- OM %>% dplyr::select(!c(nrow, dist, lon, lat))
OM <- OM %>%
pivot_longer(cols=c(2:217), names_to="OM_date", names_prefix="OM_")
OMx <- OM
OMx$OM_date <- gsub("jan", "01-01-20", OMx$OM_date)
OMx$OM_date <- gsub("feb", "01-02-20", OMx$OM_date)
OMx$OM_date <- gsub("mar", "01-03-20", OMx$OM_date)
OMx$OM_date <- gsub("apr", "01-04-20", OMx$OM_date)
OMx$OM_date <- gsub("may", "01-05-20", OMx$OM_date)
OMx$OM_date <- gsub("jun", "01-06-20", OMx$OM_date)
OMx$OM_date <- gsub("jul", "01-07-20", OMx$OM_date)
OMx$OM_date <- gsub("aug", "01-08-20", OMx$OM_date)
OMx$OM_date <- gsub("sep", "01-09-20", OMx$OM_date)
OMx$OM_date <- gsub("oct", "01-10-20", OMx$OM_date)
OMx$OM_date <- gsub("nov", "01-11-20", OMx$OM_date)
OMx$OM_date <- gsub("dec", "01-12-20", OMx$OM_date)
OMx$OM_date <- format(as.Date(OMx$OM_date, format="%d-%m-%Y"),"%Y-%m-%d")
OMx$OM_date <- as.Date(OMx$OM_date)
OM <- OMx
rm(OMx)
OM <- OM %>% mutate(start=as.IDate(OM_date))
OM <- OM %>% mutate(end=as.IDate(OM_date + months(1) - days(1)))
OM <- as.data.table(OM)
str(OM)
## Classes 'data.table' and 'data.frame': 411480 obs. of 5 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ OM_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 3.8 2.1 2.7 1.8 1.7 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
OM_5yrWtedAvg <- intervalaverage(x=OM,
y=PFF_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
OM_5yrWtedAvgx <- OM_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
OM_5yrWtedAvgx <- OM_5yrWtedAvgx %>% rename("OM"="value", "start_5yr"="start", "end_5yr"="end")
OM_5yrWtedAvgx <- OM_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(OM_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 87626 obs. of 4 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ OM : num 2.59 2.59 2.58 2.58 2.56 ...
## $ start_5yr: Date, format: "2001-08-06" "2001-09-05" ...
## $ end_5yr : Date, format: "2006-08-06" "2006-09-05" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to PFF
PFF <- left_join(PFF, OM_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly SS level estimates by satellite at nearest lon/lat to PFF patient residential addresses. These are linked to the patient ID.
outfile1 <- here("PFF_fILD_2000_2017_SS_2021_11_05.xlsx")
SS <- read_excel(outfile1)
SS <- SS %>% dplyr::select(!c(nrow, dist, lon, lat))
SS <- SS %>%
pivot_longer(cols=c(2:217), names_to="SS_date", names_prefix="SS_")
SSx <- SS
SSx$SS_date <- gsub("jan", "01-01-20", SSx$SS_date)
SSx$SS_date <- gsub("feb", "01-02-20", SSx$SS_date)
SSx$SS_date <- gsub("mar", "01-03-20", SSx$SS_date)
SSx$SS_date <- gsub("apr", "01-04-20", SSx$SS_date)
SSx$SS_date <- gsub("may", "01-05-20", SSx$SS_date)
SSx$SS_date <- gsub("jun", "01-06-20", SSx$SS_date)
SSx$SS_date <- gsub("jul", "01-07-20", SSx$SS_date)
SSx$SS_date <- gsub("aug", "01-08-20", SSx$SS_date)
SSx$SS_date <- gsub("sep", "01-09-20", SSx$SS_date)
SSx$SS_date <- gsub("oct", "01-10-20", SSx$SS_date)
SSx$SS_date <- gsub("nov", "01-11-20", SSx$SS_date)
SSx$SS_date <- gsub("dec", "01-12-20", SSx$SS_date)
SSx$SS_date <- format(as.Date(SSx$SS_date, format="%d-%m-%Y"),"%Y-%m-%d")
SSx$SS_date <- as.Date(SSx$SS_date)
SS <- SSx
rm(SSx)
SS <- SS %>% mutate(start=as.IDate(SS_date))
SS <- SS %>% mutate(end=as.IDate(SS_date + months(1) - days(1)))
SS <- as.data.table(SS)
str(SS)
## Classes 'data.table' and 'data.frame': 411480 obs. of 5 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ SS_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 0.4 0.4 0.5 0 0.4 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
SS_5yrWtedAvg <- intervalaverage(x=SS,
y=PFF_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
SS_5yrWtedAvgx <- SS_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
SS_5yrWtedAvgx <- SS_5yrWtedAvgx %>% rename("SS"="value", "start_5yr"="start", "end_5yr"="end")
SS_5yrWtedAvgx <- SS_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(SS_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 87626 obs. of 4 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ SS : num 0.208 0.207 0.203 0.198 0.193 ...
## $ start_5yr: Date, format: "2001-08-06" "2001-09-05" ...
## $ end_5yr : Date, format: "2006-08-06" "2006-09-05" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to PFF
PFF <- left_join(PFF, SS_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly Soil level estimates by satellite at nearest lon/lat to PFF patient residential addresses. These are linked to the patient ID.
outfile1 <- here("PFF_fILD_2000_2017_Soil_2021_11_05.xlsx")
Soil <- read_excel(outfile1)
Soil <- Soil %>% dplyr::select(!c(nrow, dist, lon, lat))
Soil <- Soil %>%
pivot_longer(cols=c(2:217), names_to="Soil_date", names_prefix="soil_")
Soilx <- Soil
Soilx$Soil_date <- gsub("jan", "01-01-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("feb", "01-02-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("mar", "01-03-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("apr", "01-04-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("may", "01-05-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("jun", "01-06-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("jul", "01-07-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("aug", "01-08-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("sep", "01-09-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("oct", "01-10-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("nov", "01-11-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("dec", "01-12-20", Soilx$Soil_date)
Soilx$Soil_date <- format(as.Date(Soilx$Soil_date, format="%d-%m-%Y"),"%Y-%m-%d")
Soilx$Soil_date <- as.Date(Soilx$Soil_date)
Soil <- Soilx
rm(Soilx)
Soil <- Soil %>% mutate(start=as.IDate(Soil_date))
Soil <- Soil %>% mutate(end=as.IDate(Soil_date + months(1) - days(1)))
Soil <- as.data.table(Soil)
str(Soil)
## Classes 'data.table' and 'data.frame': 411480 obs. of 5 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Soil_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 0.4 0.3 0.5 0.5 0.5 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
Soil_5yrWtedAvg <- intervalaverage(x=Soil,
y=PFF_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
Soil_5yrWtedAvgx <- Soil_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
Soil_5yrWtedAvgx <- Soil_5yrWtedAvgx %>% rename("Soil"="value", "start_5yr"="start", "end_5yr"="end")
Soil_5yrWtedAvgx <- Soil_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(Soil_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 87626 obs. of 4 variables:
## $ ID : num 1 1 1 1 1 1 1 1 1 1 ...
## $ Soil : num 0.462 0.459 0.46 0.463 0.457 ...
## $ start_5yr: Date, format: "2001-08-06" "2001-09-05" ...
## $ end_5yr : Date, format: "2006-08-06" "2006-09-05" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to PFF
PFF <- left_join(PFF, Soil_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly PM level estimates by satellite at nearest lon/lat to CAREPF patient residential addresses. These are linked to the patient ID.
outfile1 <- here("CAREPF_fILD_2000_2018_PM25_2021_09_16.xlsx")
PM <- read_excel(outfile1)
Here I am importing the file which I used for my CIMD work that contains the baseline clinical and demographic data for patients who have CIMD
outfile2 <- here("CAREPF_fILDPts_BaselineData_2021_10_22.xlsx")
CARE <- read_excel(outfile2)
PM <- PM %>% dplyr::select(!c(nrow, dist, lon, lat))
colnames(PM)
## [1] "ID" "PM25_jan00" "PM25_feb00" "PM25_mar00" "PM25_apr00"
## [6] "PM25_may00" "PM25_jun00" "PM25_jul00" "PM25_aug00" "PM25_sep00"
## [11] "PM25_oct00" "PM25_nov00" "PM25_dec00" "PM25_jan01" "PM25_feb01"
## [16] "PM25_mar01" "PM25_apr01" "PM25_may01" "PM25_jun01" "PM25_jul01"
## [21] "PM25_aug01" "PM25_sep01" "PM25_oct01" "PM25_nov01" "PM25_dec01"
## [26] "PM25_jan02" "PM25_feb02" "PM25_mar02" "PM25_apr02" "PM25_may02"
## [31] "PM25_jun02" "PM25_jul02" "PM25_aug02" "PM25_sep02" "PM25_oct02"
## [36] "PM25_nov02" "PM25_dec02" "PM25_jan03" "PM25_feb03" "PM25_mar03"
## [41] "PM25_apr03" "PM25_may03" "PM25_jun03" "PM25_jul03" "PM25_aug03"
## [46] "PM25_sep03" "PM25_oct03" "PM25_nov03" "PM25_dec03" "PM25_jan04"
## [51] "PM25_feb04" "PM25_mar04" "PM25_apr04" "PM25_may04" "PM25_jun04"
## [56] "PM25_jul04" "PM25_aug04" "PM25_sep04" "PM25_oct04" "PM25_nov04"
## [61] "PM25_dec04" "PM25_jan05" "PM25_feb05" "PM25_mar05" "PM25_apr05"
## [66] "PM25_may05" "PM25_jun05" "PM25_jul05" "PM25_aug05" "PM25_sep05"
## [71] "PM25_oct05" "PM25_nov05" "PM25_dec05" "PM25_jan06" "PM25_feb06"
## [76] "PM25_mar06" "PM25_apr06" "PM25_may06" "PM25_jun06" "PM25_jul06"
## [81] "PM25_aug06" "PM25_sep06" "PM25_oct06" "PM25_nov06" "PM25_dec06"
## [86] "PM25_jan07" "PM25_feb07" "PM25_mar07" "PM25_apr07" "PM25_may07"
## [91] "PM25_jun07" "PM25_jul07" "PM25_aug07" "PM25_sep07" "PM25_oct07"
## [96] "PM25_nov07" "PM25_dec07" "PM25_jan08" "PM25_feb08" "PM25_mar08"
## [101] "PM25_apr08" "PM25_may08" "PM25_jun08" "PM25_jul08" "PM25_aug08"
## [106] "PM25_sep08" "PM25_oct08" "PM25_nov08" "PM25_dec08" "PM25_jan09"
## [111] "PM25_feb09" "PM25_mar09" "PM25_apr09" "PM25_may09" "PM25_jun09"
## [116] "PM25_jul09" "PM25_aug09" "PM25_sep09" "PM25_oct09" "PM25_nov09"
## [121] "PM25_dec09" "PM25_jan10" "PM25_feb10" "PM25_mar10" "PM25_apr10"
## [126] "PM25_may10" "PM25_jun10" "PM25_jul10" "PM25_aug10" "PM25_sep10"
## [131] "PM25_oct10" "PM25_nov10" "PM25_dec10" "PM25_jan11" "PM25_feb11"
## [136] "PM25_mar11" "PM25_apr11" "PM25_may11" "PM25_jun11" "PM25_jul11"
## [141] "PM25_aug11" "PM25_sep11" "PM25_oct11" "PM25_nov11" "PM25_dec11"
## [146] "PM25_jan12" "PM25_feb12" "PM25_mar12" "PM25_apr12" "PM25_may12"
## [151] "PM25_jun12" "PM25_jul12" "PM25_aug12" "PM25_sep12" "PM25_oct12"
## [156] "PM25_nov12" "PM25_dec12" "PM25_jan13" "PM25_feb13" "PM25_mar13"
## [161] "PM25_apr13" "PM25_may13" "PM25_jun13" "PM25_jul13" "PM25_aug13"
## [166] "PM25_sep13" "PM25_oct13" "PM25_nov13" "PM25_dec13" "PM25_jan14"
## [171] "PM25_feb14" "PM25_mar14" "PM25_apr14" "PM25_may14" "PM25_jun14"
## [176] "PM25_jul14" "PM25_aug14" "PM25_sep14" "PM25_oct14" "PM25_nov14"
## [181] "PM25_dec14" "PM25_jan15" "PM25_feb15" "PM25_mar15" "PM25_apr15"
## [186] "PM25_may15" "PM25_jun15" "PM25_jul15" "PM25_aug15" "PM25_sep15"
## [191] "PM25_oct15" "PM25_nov15" "PM25_dec15" "PM25_jan16" "PM25_feb16"
## [196] "PM25_mar16" "PM25_apr16" "PM25_may16" "PM25_jun16" "PM25_jul16"
## [201] "PM25_aug16" "PM25_sep16" "PM25_oct16" "PM25_nov16" "PM25_dec16"
## [206] "PM25_jan17" "PM25_feb17" "PM25_mar17" "PM25_apr17" "PM25_may17"
## [211] "PM25_jun17" "PM25_jul17" "PM25_aug17" "PM25_sep17" "PM25_oct17"
## [216] "PM25_nov17" "PM25_dec17" "PM25_jan18" "PM25_feb18" "PM25_mar18"
## [221] "PM25_apr18" "PM25_may18" "PM25_jun18" "PM25_aug18" "PM25_sep18"
## [226] "PM25_oct18" "PM25_nov18" "PM25_dec18"
PM <- PM %>%
pivot_longer(cols=c(2:228), names_to="PM_date", names_prefix="PM25_")
PMx <- PM
PMx$PM_date <- gsub("jan", "01-01-20", PMx$PM_date)
PMx$PM_date <- gsub("feb", "01-02-20", PMx$PM_date)
PMx$PM_date <- gsub("mar", "01-03-20", PMx$PM_date)
PMx$PM_date <- gsub("apr", "01-04-20", PMx$PM_date)
PMx$PM_date <- gsub("may", "01-05-20", PMx$PM_date)
PMx$PM_date <- gsub("jun", "01-06-20", PMx$PM_date)
PMx$PM_date <- gsub("jul", "01-07-20", PMx$PM_date)
PMx$PM_date <- gsub("aug", "01-08-20", PMx$PM_date)
PMx$PM_date <- gsub("sep", "01-09-20", PMx$PM_date)
PMx$PM_date <- gsub("oct", "01-10-20", PMx$PM_date)
PMx$PM_date <- gsub("nov", "01-11-20", PMx$PM_date)
PMx$PM_date <- gsub("dec", "01-12-20", PMx$PM_date)
PMx$PM_date <- format(as.Date(PMx$PM_date, format="%d-%m-%Y"),"%Y-%m-%d")
PMx$PM_date <- as.Date(PMx$PM_date)
PM <- PMx
rm(PMx)
Extracting year of diagnosis and year of death/transplant/censoring
#Start with the year of diagnosis
CARE <- CARE %>%
mutate(dx_yrmo = format(as.Date(CARE$dx_date, format="%Y-%m-%d"),"%Y-%m"))
CARE <- CARE %>%
mutate(dx_yr = format(as.Date(CARE$dx_date, format="%Y-%m-%d"),"%Y"))
CARE$dx_yr <- as.numeric(CARE$dx_yr)
#Then the year of death or lung transplant
CARE <- CARE %>%
mutate(deathORtx_date = if_else(!is.na(tx_date), tx_date, death_date))
CARE <- CARE %>%
mutate(deathORtx_yrmo = format(as.Date(CARE$deathORtx_date, format="%Y-%m-%d"),"%Y-%m"))
#Then the year the records were last updated (i.e. year of censoring)
CARE <- CARE %>%
mutate(DeathTxCensor_date = if_else(!is.na(deathORtx_date), deathORtx_date, last_updated))
CARE <- CARE %>%
mutate(censor_yrmo = format(as.Date(CARE$DeathTxCensor_date, format="%Y-%m-%d"),"%Y-%m"))
CARE <- CARE %>% dplyr::select(c(ID, site, age_dx, sex, smokeHx, dich_smoking, race, dich_Race, avg_s, dx_group, dx, dob, dx_date, death_date, tx_date, DeathTxCensor_date, last_updated, pft_date, fvc_pct, dlco_pct, pft_timefromdx, status, died, txed, deadORtx, time_DeathTxCensor, dx_yr))
CARE <- CARE %>%
mutate_at(c("dob","death_date", "last_updated", "tx_date", "dx_date", "pft_date", "DeathTxCensor_date"), as.Date)
str(CARE)
## tibble [3,389 × 27] (S3: tbl_df/tbl/data.frame)
## $ ID : num [1:3389] 10200257 10200069 10200266 10200336 10200229 ...
## $ site : num [1:3389] 102 102 102 102 102 102 102 102 102 102 ...
## $ age_dx : num [1:3389] 32.3 69.2 53.2 54.8 63.2 ...
## $ sex : chr [1:3389] "F" "M" "M" "M" ...
## $ smokeHx : chr [1:3389] "Former" "Former" "Never" "Never" ...
## $ dich_smoking : chr [1:3389] "Ever" "Ever" "Never" "Never" ...
## $ race : chr [1:3389] "W" "W" "W" "W" ...
## $ dich_Race : chr [1:3389] "White" "White" "White" "White" ...
## $ avg_s : num [1:3389] -0.797 -0.491 -0.881 -1.005 -1.005 ...
## $ dx_group : chr [1:3389] "CTD-ILD" "CTD-ILD" "CTD-ILD" "CTD-ILD" ...
## $ dx : chr [1:3389] "ANTISYNTHETASE" "RA_ILD" "UNDIFF_CTD_ILD" "SJOGRENS_ILD" ...
## $ dob : Date[1:3389], format: "1980-08-27" "1947-05-04" ...
## $ dx_date : Date[1:3389], format: "2012-12-15" "2016-07-14" ...
## $ death_date : Date[1:3389], format: NA "2017-07-12" ...
## $ tx_date : Date[1:3389], format: NA NA ...
## $ DeathTxCensor_date: Date[1:3389], format: "2019-01-17" "2017-07-12" ...
## $ last_updated : Date[1:3389], format: "2019-01-17" "2017-07-12" ...
## $ pft_date : Date[1:3389], format: "2012-11-15" "2016-07-15" ...
## $ fvc_pct : num [1:3389] 79 105 97 78 80 63 62 65 96 82 ...
## $ dlco_pct : num [1:3389] 75 57 97 74 NA 44 66 49 88 64 ...
## $ pft_timefromdx : num [1:3389] -0.08214 0.00274 0 0 0.49555 ...
## $ status : num [1:3389] 0 1 0 0 1 0 1 0 0 0 ...
## $ died : chr [1:3389] "0" "1" "0" "0" ...
## $ txed : chr [1:3389] "0" "0" "0" "0" ...
## $ deadORtx : chr [1:3389] "0" "1" "0" "0" ...
## $ time_DeathTxCensor: num [1:3389] 6.089 0.994 2.91 3.365 4.6 ...
## $ dx_yr : num [1:3389] 2012 2016 2017 2017 2014 ...
Need to correct smoking variables
CARE$smokeHx <- as.character(CARE$smokeHx)
CARE <- CARE %>% mutate(smokeHx1=if_else(is.na(smokeHx), "Unknown", smokeHx))
#now need to make new dich_smoking category
CARE$dich_smoking <- as.character(CARE$dich_smoking)
CARE <- CARE %>% mutate(dich_smoking1=if_else(is.na(dich_smoking),"Unknown", dich_smoking))
Now need to remove old smoking variables and rename new ones
CARE <- CARE %>% dplyr::select(-c(smokeHx, dich_smoking))
CARE <- CARE %>% rename(c("smokeHx"="smokeHx1", "dich_smoking"="dich_smoking1"))
CARE$smokeHx <- as.factor(CARE$smokeHx)
CARE$dich_smoking <- as.factor(CARE$dich_smoking)
CARE$smokeHx <- fct_relevel(CARE$smokeHx, c("Never","Former","Always","Unknown"))
CARE$dich_smoking <- fct_relevel(CARE$dich_smoking, c("Never","Ever","Unknown"))
Need to correct other factor variables
CARE$sex <- fct_relevel(CARE$sex, c("M","F"))
CARE$race <- fct_relevel(CARE$race, c("W","B","A","N","U"))
CARE$dich_Race <- fct_relevel(CARE$dich_Race, c("White","Non-White"))
CARE$dx <- fct_relevel(CARE$dx, c("IPF"))
CARE$dx_group <- fct_relevel(CARE$dx_group, c("IPF"))
CARE <- CARE %>% mutate_at(c("status","died", "txed", "deadORtx", "site"), as.factor)
CARE <- CARE %>% mutate(dx_IPF=ifelse(dx=="IPF", "IPF", "not_IPF"))
CARE$dx_IPF <- fct_relevel(CARE$dx_IPF, c("IPF"))
Creating this empirical cumulative distribution will allow us to combine the analyses of all three cohorts even though the measurements for disadvantage are different between the three.
plot(ecdf(CARE$avg_s))
CARE$disadv <- ecdf(CARE$avg_s)(CARE$avg_s)
Take down CARE dataset to the 3389 patients with complete data
IDs <- as.data.table(unique(PM$ID))
IDs <- IDs %>% rename("ID"="V1")
CARE <- left_join(IDs, CARE, by="ID")
CARE <- CARE %>% mutate(days_DeathTxCensor=(time_DeathTxCensor*365.25))
Longest time_DeathTxCensor= 9546days
30-day longest time interval would be 9570 days
start <- seq(1, 9541, by = 30)
start
## [1] 1 31 61 91 121 151 181 211 241 271 301 331 361 391 421
## [16] 451 481 511 541 571 601 631 661 691 721 751 781 811 841 871
## [31] 901 931 961 991 1021 1051 1081 1111 1141 1171 1201 1231 1261 1291 1321
## [46] 1351 1381 1411 1441 1471 1501 1531 1561 1591 1621 1651 1681 1711 1741 1771
## [61] 1801 1831 1861 1891 1921 1951 1981 2011 2041 2071 2101 2131 2161 2191 2221
## [76] 2251 2281 2311 2341 2371 2401 2431 2461 2491 2521 2551 2581 2611 2641 2671
## [91] 2701 2731 2761 2791 2821 2851 2881 2911 2941 2971 3001 3031 3061 3091 3121
## [106] 3151 3181 3211 3241 3271 3301 3331 3361 3391 3421 3451 3481 3511 3541 3571
## [121] 3601 3631 3661 3691 3721 3751 3781 3811 3841 3871 3901 3931 3961 3991 4021
## [136] 4051 4081 4111 4141 4171 4201 4231 4261 4291 4321 4351 4381 4411 4441 4471
## [151] 4501 4531 4561 4591 4621 4651 4681 4711 4741 4771 4801 4831 4861 4891 4921
## [166] 4951 4981 5011 5041 5071 5101 5131 5161 5191 5221 5251 5281 5311 5341 5371
## [181] 5401 5431 5461 5491 5521 5551 5581 5611 5641 5671 5701 5731 5761 5791 5821
## [196] 5851 5881 5911 5941 5971 6001 6031 6061 6091 6121 6151 6181 6211 6241 6271
## [211] 6301 6331 6361 6391 6421 6451 6481 6511 6541 6571 6601 6631 6661 6691 6721
## [226] 6751 6781 6811 6841 6871 6901 6931 6961 6991 7021 7051 7081 7111 7141 7171
## [241] 7201 7231 7261 7291 7321 7351 7381 7411 7441 7471 7501 7531 7561 7591 7621
## [256] 7651 7681 7711 7741 7771 7801 7831 7861 7891 7921 7951 7981 8011 8041 8071
## [271] 8101 8131 8161 8191 8221 8251 8281 8311 8341 8371 8401 8431 8461 8491 8521
## [286] 8551 8581 8611 8641 8671 8701 8731 8761 8791 8821 8851 8881 8911 8941 8971
## [301] 9001 9031 9061 9091 9121 9151 9181 9211 9241 9271 9301 9331 9361 9391 9421
## [316] 9451 9481 9511 9541
end <- seq(30, 9570, by = 30)
end
## [1] 30 60 90 120 150 180 210 240 270 300 330 360 390 420 450
## [16] 480 510 540 570 600 630 660 690 720 750 780 810 840 870 900
## [31] 930 960 990 1020 1050 1080 1110 1140 1170 1200 1230 1260 1290 1320 1350
## [46] 1380 1410 1440 1470 1500 1530 1560 1590 1620 1650 1680 1710 1740 1770 1800
## [61] 1830 1860 1890 1920 1950 1980 2010 2040 2070 2100 2130 2160 2190 2220 2250
## [76] 2280 2310 2340 2370 2400 2430 2460 2490 2520 2550 2580 2610 2640 2670 2700
## [91] 2730 2760 2790 2820 2850 2880 2910 2940 2970 3000 3030 3060 3090 3120 3150
## [106] 3180 3210 3240 3270 3300 3330 3360 3390 3420 3450 3480 3510 3540 3570 3600
## [121] 3630 3660 3690 3720 3750 3780 3810 3840 3870 3900 3930 3960 3990 4020 4050
## [136] 4080 4110 4140 4170 4200 4230 4260 4290 4320 4350 4380 4410 4440 4470 4500
## [151] 4530 4560 4590 4620 4650 4680 4710 4740 4770 4800 4830 4860 4890 4920 4950
## [166] 4980 5010 5040 5070 5100 5130 5160 5190 5220 5250 5280 5310 5340 5370 5400
## [181] 5430 5460 5490 5520 5550 5580 5610 5640 5670 5700 5730 5760 5790 5820 5850
## [196] 5880 5910 5940 5970 6000 6030 6060 6090 6120 6150 6180 6210 6240 6270 6300
## [211] 6330 6360 6390 6420 6450 6480 6510 6540 6570 6600 6630 6660 6690 6720 6750
## [226] 6780 6810 6840 6870 6900 6930 6960 6990 7020 7050 7080 7110 7140 7170 7200
## [241] 7230 7260 7290 7320 7350 7380 7410 7440 7470 7500 7530 7560 7590 7620 7650
## [256] 7680 7710 7740 7770 7800 7830 7860 7890 7920 7950 7980 8010 8040 8070 8100
## [271] 8130 8160 8190 8220 8250 8280 8310 8340 8370 8400 8430 8460 8490 8520 8550
## [286] 8580 8610 8640 8670 8700 8730 8760 8790 8820 8850 8880 8910 8940 8970 9000
## [301] 9030 9060 9090 9120 9150 9180 9210 9240 9270 9300 9330 9360 9390 9420 9450
## [316] 9480 9510 9540 9570
Repeat the list of intervals 3389 times (number of patients in CARE)
start <- rep(start, times=3389)
end <- rep(end, times=3389)
intervals <- as.data.frame(cbind(start, end))
Add ID column to intervals
IDs <- rep(CARE$ID, each=319)
intervals <- as.data.frame(cbind(IDs, intervals))
intervals <- intervals %>% rename("ID"="IDs")
Join CARE and intervals
CARE <- left_join(intervals, CARE, by="ID")
Determine if event occurred during interval
CARE <- CARE %>% mutate(event=if_else((days_DeathTxCensor>=start & days_DeathTxCensor<=end), 1, 0))
Now will add date intervals for 5yr start and end times)
CARE <- CARE %>% mutate(end_5yr=(dx_date + days(end)))
CARE <- CARE %>% mutate(start_5yr=(end_5yr - days(1826)))
Now need to remove any rows where end_5yr is > date_DeathTxCensor (Amanda indicated need to remove rows where start_5yr > date_DeathTxCensor, but I don’t think this is correct because then we’d be assigning exposures to patients where they were alive/not transplanted for <5yrs for that exposure - need to double check with her)
CARE <- CARE %>% filter(!(end_5yr>DeathTxCensor_date & event!=1))
PM <- PM %>% mutate(start=as.IDate(PM_date))
PM <- PM %>% mutate(end=as.IDate(PM_date + months(1) - days(1)))
PM <- as.data.table(PM)
str(PM)
## Classes 'data.table' and 'data.frame': 769303 obs. of 5 variables:
## $ ID : num 10100126 10100126 10100126 10100126 10100126 ...
## $ PM_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 6 7.4 7.9 5.8 8.3 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
Creating a list of intervals we want to calculate exposures for:
CARE_intervals <- CARE %>% dplyr::select(ID, start_5yr, end_5yr)
CARE_intervals <- CARE_intervals %>% mutate_at(c("start_5yr", "end_5yr"), as.IDate)
CARE_intervals <- CARE_intervals %>% rename(c("start"="start_5yr", "end"="end_5yr"))
CARE_intervals <- as.data.table(CARE_intervals)
str(CARE_intervals)
## Classes 'data.table' and 'data.frame': 160215 obs. of 3 variables:
## $ ID : num 10100126 10100126 10100126 10100331 10100331 ...
## $ start: IDate, format: "2012-09-10" "2012-10-10" ...
## $ end : IDate, format: "2017-09-10" "2017-10-10" ...
## - attr(*, ".internal.selfref")=<externalptr>
PM_5yrWtedAvg <- intervalaverage(x=PM,
y=CARE_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
PM_5yrWtedAvgx <- PM_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
PM_5yrWtedAvgx <- PM_5yrWtedAvgx %>% rename("PM"="value", "start_5yr"="start", "end_5yr"="end")
PM_5yrWtedAvgx <- PM_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(PM_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 160215 obs. of 4 variables:
## $ ID : num 10100001 10100001 10100001 10100001 10100001 ...
## $ PM : num 7.45 7.42 7.4 7.37 7.33 ...
## $ start_5yr: Date, format: "2006-02-20" "2006-03-22" ...
## $ end_5yr : Date, format: "2011-02-20" "2011-03-22" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to CARE
CARE <- left_join(CARE, PM_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly SO4 level estimates by satellite at nearest lon/lat to CARE patient residential addresses. These are linked to the patient ID.
outfile1 <- here("CARE_fILD_2000_2017_SO4_2021_11_05.xlsx")
SO4 <- read_excel(outfile1)
SO4 <- SO4 %>% dplyr::select(!c(nrow, dist, lon, lat))
SO4 <- SO4 %>%
pivot_longer(cols=c(2:217), names_to="SO4_date", names_prefix="SO4_")
SO4x <- SO4
SO4x$SO4_date <- gsub("jan", "01-01-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("feb", "01-02-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("mar", "01-03-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("apr", "01-04-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("may", "01-05-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("jun", "01-06-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("jul", "01-07-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("aug", "01-08-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("sep", "01-09-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("oct", "01-10-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("nov", "01-11-20", SO4x$SO4_date)
SO4x$SO4_date <- gsub("dec", "01-12-20", SO4x$SO4_date)
SO4x$SO4_date <- format(as.Date(SO4x$SO4_date, format="%d-%m-%Y"),"%Y-%m-%d")
SO4x$SO4_date <- as.Date(SO4x$SO4_date)
SO4 <- SO4x
rm(SO4x)
SO4 <- SO4 %>% mutate(start=as.IDate(SO4_date))
SO4 <- SO4 %>% mutate(end=as.IDate(SO4_date + months(1) - days(1)))
SO4 <- as.data.table(SO4)
str(SO4)
## Classes 'data.table' and 'data.frame': 732024 obs. of 5 variables:
## $ ID : num 10200257 10200257 10200257 10200257 10200257 ...
## $ SO4_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 1.3 0.4 0.2 0.5 0.6 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
SO4_5yrWtedAvg <- intervalaverage(x=SO4,
y=CARE_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
SO4_5yrWtedAvgx <- SO4_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
SO4_5yrWtedAvgx <- SO4_5yrWtedAvgx %>% rename("SO4"="value", "start_5yr"="start", "end_5yr"="end")
SO4_5yrWtedAvgx <- SO4_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(SO4_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 160215 obs. of 4 variables:
## $ ID : num 10100001 10100001 10100001 10100001 10100001 ...
## $ SO4 : num 2.03 2.01 2.01 1.99 1.97 ...
## $ start_5yr: Date, format: "2006-02-20" "2006-03-22" ...
## $ end_5yr : Date, format: "2011-02-20" "2011-03-22" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to CARE
CARE <- left_join(CARE, SO4_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly NO3 level estimates by satellite at nearest lon/lat to CARE patient residential addresses. These are linked to the patient ID.
outfile1 <- here("CARE_fILD_2000_2017_NO3_2021_11_05.xlsx")
NO3 <- read_excel(outfile1)
NO3 <- NO3 %>% dplyr::select(!c(nrow, dist, lon, lat))
NO3 <- NO3 %>%
pivot_longer(cols=c(2:217), names_to="NO3_date", names_prefix="NIT_")
NO3x <- NO3
NO3x$NO3_date <- gsub("jan", "01-01-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("feb", "01-02-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("mar", "01-03-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("apr", "01-04-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("may", "01-05-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("jun", "01-06-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("jul", "01-07-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("aug", "01-08-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("sep", "01-09-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("oct", "01-10-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("nov", "01-11-20", NO3x$NO3_date)
NO3x$NO3_date <- gsub("dec", "01-12-20", NO3x$NO3_date)
NO3x$NO3_date <- format(as.Date(NO3x$NO3_date, format="%d-%m-%Y"),"%Y-%m-%d")
NO3x$NO3_date <- as.Date(NO3x$NO3_date)
NO3 <- NO3x
rm(NO3x)
NO3 <- NO3 %>% mutate(start=as.IDate(NO3_date))
NO3 <- NO3 %>% mutate(end=as.IDate(NO3_date + months(1) - days(1)))
NO3 <- as.data.table(NO3)
str(NO3)
## Classes 'data.table' and 'data.frame': 732024 obs. of 5 variables:
## $ ID : num 10200257 10200257 10200257 10200257 10200257 ...
## $ NO3_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 0.9 0.1 0.1 0.8 0.6 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
NO3_5yrWtedAvg <- intervalaverage(x=NO3,
y=CARE_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
NO3_5yrWtedAvgx <- NO3_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
NO3_5yrWtedAvgx <- NO3_5yrWtedAvgx %>% rename("NO3"="value", "start_5yr"="start", "end_5yr"="end")
NO3_5yrWtedAvgx <- NO3_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(NO3_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 160215 obs. of 4 variables:
## $ ID : num 10100001 10100001 10100001 10100001 10100001 ...
## $ NO3 : num 0.889 0.888 0.885 0.885 0.887 ...
## $ start_5yr: Date, format: "2006-02-20" "2006-03-22" ...
## $ end_5yr : Date, format: "2011-02-20" "2011-03-22" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to CARE
CARE <- left_join(CARE, NO3_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly NH4 level estimates by satellite at nearest lon/lat to CARE patient residential addresses. These are linked to the patient ID.
outfile1 <- here("CARE_fILD_2000_2017_NH4_2021_11_05.xlsx")
NH4 <- read_excel(outfile1)
NH4 <- NH4 %>% dplyr::select(!c(nrow, dist, lon, lat))
NH4 <- NH4 %>%
pivot_longer(cols=c(2:217), names_to="NH4_date", names_prefix="NH4_")
NH4x <- NH4
NH4x$NH4_date <- gsub("jan", "01-01-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("feb", "01-02-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("mar", "01-03-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("apr", "01-04-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("may", "01-05-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("jun", "01-06-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("jul", "01-07-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("aug", "01-08-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("sep", "01-09-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("oct", "01-10-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("nov", "01-11-20", NH4x$NH4_date)
NH4x$NH4_date <- gsub("dec", "01-12-20", NH4x$NH4_date)
NH4x$NH4_date <- format(as.Date(NH4x$NH4_date, format="%d-%m-%Y"),"%Y-%m-%d")
NH4x$NH4_date <- as.Date(NH4x$NH4_date)
NH4 <- NH4x
rm(NH4x)
NH4 <- NH4 %>% mutate(start=as.IDate(NH4_date))
NH4 <- NH4 %>% mutate(end=as.IDate(NH4_date + months(1) - days(1)))
NH4 <- as.data.table(NH4)
str(NH4)
## Classes 'data.table' and 'data.frame': 732024 obs. of 5 variables:
## $ ID : num 10200257 10200257 10200257 10200257 10200257 ...
## $ NH4_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 0.8 0.4 0.2 0.3 0.4 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
NH4_5yrWtedAvg <- intervalaverage(x=NH4,
y=CARE_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
NH4_5yrWtedAvgx <- NH4_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
NH4_5yrWtedAvgx <- NH4_5yrWtedAvgx %>% rename("NH4"="value", "start_5yr"="start", "end_5yr"="end")
NH4_5yrWtedAvgx <- NH4_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(NH4_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 160215 obs. of 4 variables:
## $ ID : num 10100001 10100001 10100001 10100001 10100001 ...
## $ NH4 : num 0.89 0.883 0.88 0.871 0.861 ...
## $ start_5yr: Date, format: "2006-02-20" "2006-03-22" ...
## $ end_5yr : Date, format: "2011-02-20" "2011-03-22" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to CARE
CARE <- left_join(CARE, NH4_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly BC level estimates by satellite at nearest lon/lat to CARE patient residential addresses. These are linked to the patient ID.
outfile1 <- here("CARE_fILD_2000_2017_BC_2021_11_05.xlsx")
BC <- read_excel(outfile1)
BC <- BC %>% dplyr::select(!c(nrow, dist, lon, lat))
BC <- BC %>%
pivot_longer(cols=c(2:217), names_to="BC_date", names_prefix="BC_")
BCx <- BC
BCx$BC_date <- gsub("jan", "01-01-20", BCx$BC_date)
BCx$BC_date <- gsub("feb", "01-02-20", BCx$BC_date)
BCx$BC_date <- gsub("mar", "01-03-20", BCx$BC_date)
BCx$BC_date <- gsub("apr", "01-04-20", BCx$BC_date)
BCx$BC_date <- gsub("may", "01-05-20", BCx$BC_date)
BCx$BC_date <- gsub("jun", "01-06-20", BCx$BC_date)
BCx$BC_date <- gsub("jul", "01-07-20", BCx$BC_date)
BCx$BC_date <- gsub("aug", "01-08-20", BCx$BC_date)
BCx$BC_date <- gsub("sep", "01-09-20", BCx$BC_date)
BCx$BC_date <- gsub("oct", "01-10-20", BCx$BC_date)
BCx$BC_date <- gsub("nov", "01-11-20", BCx$BC_date)
BCx$BC_date <- gsub("dec", "01-12-20", BCx$BC_date)
BCx$BC_date <- format(as.Date(BCx$BC_date, format="%d-%m-%Y"),"%Y-%m-%d")
BCx$BC_date <- as.Date(BCx$BC_date)
BC <- BCx
rm(BCx)
BC <- BC %>% mutate(start=as.IDate(BC_date))
BC <- BC %>% mutate(end=as.IDate(BC_date + months(1) - days(1)))
BC <- as.data.table(BC)
str(BC)
## Classes 'data.table' and 'data.frame': 732024 obs. of 5 variables:
## $ ID : num 10200257 10200257 10200257 10200257 10200257 ...
## $ BC_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 0.4 0.3 0.2 0.2 0.2 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
BC_5yrWtedAvg <- intervalaverage(x=BC,
y=CARE_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
BC_5yrWtedAvgx <- BC_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
BC_5yrWtedAvgx <- BC_5yrWtedAvgx %>% rename("BC"="value", "start_5yr"="start", "end_5yr"="end")
BC_5yrWtedAvgx <- BC_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(BC_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 160215 obs. of 4 variables:
## $ ID : num 10100001 10100001 10100001 10100001 10100001 ...
## $ BC : num 0.534 0.531 0.529 0.525 0.52 ...
## $ start_5yr: Date, format: "2006-02-20" "2006-03-22" ...
## $ end_5yr : Date, format: "2011-02-20" "2011-03-22" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to CARE
CARE <- left_join(CARE, BC_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly OM level estimates by satellite at nearest lon/lat to CARE patient residential addresses. These are linked to the patient ID.
outfile1 <- here("CARE_fILD_2000_2017_OM_2021_11_05.xlsx")
OM <- read_excel(outfile1)
OM <- OM %>% dplyr::select(!c(nrow, dist, lon, lat))
OM <- OM %>%
pivot_longer(cols=c(2:217), names_to="OM_date", names_prefix="OM_")
OMx <- OM
OMx$OM_date <- gsub("jan", "01-01-20", OMx$OM_date)
OMx$OM_date <- gsub("feb", "01-02-20", OMx$OM_date)
OMx$OM_date <- gsub("mar", "01-03-20", OMx$OM_date)
OMx$OM_date <- gsub("apr", "01-04-20", OMx$OM_date)
OMx$OM_date <- gsub("may", "01-05-20", OMx$OM_date)
OMx$OM_date <- gsub("jun", "01-06-20", OMx$OM_date)
OMx$OM_date <- gsub("jul", "01-07-20", OMx$OM_date)
OMx$OM_date <- gsub("aug", "01-08-20", OMx$OM_date)
OMx$OM_date <- gsub("sep", "01-09-20", OMx$OM_date)
OMx$OM_date <- gsub("oct", "01-10-20", OMx$OM_date)
OMx$OM_date <- gsub("nov", "01-11-20", OMx$OM_date)
OMx$OM_date <- gsub("dec", "01-12-20", OMx$OM_date)
OMx$OM_date <- format(as.Date(OMx$OM_date, format="%d-%m-%Y"),"%Y-%m-%d")
OMx$OM_date <- as.Date(OMx$OM_date)
OM <- OMx
rm(OMx)
OM <- OM %>% mutate(start=as.IDate(OM_date))
OM <- OM %>% mutate(end=as.IDate(OM_date + months(1) - days(1)))
OM <- as.data.table(OM)
str(OM)
## Classes 'data.table' and 'data.frame': 732024 obs. of 5 variables:
## $ ID : num 10200257 10200257 10200257 10200257 10200257 ...
## $ OM_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 2 1.1 0.4 0.7 1 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
OM_5yrWtedAvg <- intervalaverage(x=OM,
y=CARE_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
OM_5yrWtedAvgx <- OM_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
OM_5yrWtedAvgx <- OM_5yrWtedAvgx %>% rename("OM"="value", "start_5yr"="start", "end_5yr"="end")
OM_5yrWtedAvgx <- OM_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(OM_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 160215 obs. of 4 variables:
## $ ID : num 10100001 10100001 10100001 10100001 10100001 ...
## $ OM : num 2.24 2.22 2.21 2.2 2.19 ...
## $ start_5yr: Date, format: "2006-02-20" "2006-03-22" ...
## $ end_5yr : Date, format: "2011-02-20" "2011-03-22" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to CARE
CARE <- left_join(CARE, OM_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly SS level estimates by satellite at nearest lon/lat to CARE patient residential addresses. These are linked to the patient ID.
outfile1 <- here("CARE_fILD_2000_2017_SS_2021_11_05.xlsx")
SS <- read_excel(outfile1)
SS <- SS %>% dplyr::select(!c(nrow, dist, lon, lat))
SS <- SS %>%
pivot_longer(cols=c(2:217), names_to="SS_date", names_prefix="SS_")
SSx <- SS
SSx$SS_date <- gsub("jan", "01-01-20", SSx$SS_date)
SSx$SS_date <- gsub("feb", "01-02-20", SSx$SS_date)
SSx$SS_date <- gsub("mar", "01-03-20", SSx$SS_date)
SSx$SS_date <- gsub("apr", "01-04-20", SSx$SS_date)
SSx$SS_date <- gsub("may", "01-05-20", SSx$SS_date)
SSx$SS_date <- gsub("jun", "01-06-20", SSx$SS_date)
SSx$SS_date <- gsub("jul", "01-07-20", SSx$SS_date)
SSx$SS_date <- gsub("aug", "01-08-20", SSx$SS_date)
SSx$SS_date <- gsub("sep", "01-09-20", SSx$SS_date)
SSx$SS_date <- gsub("oct", "01-10-20", SSx$SS_date)
SSx$SS_date <- gsub("nov", "01-11-20", SSx$SS_date)
SSx$SS_date <- gsub("dec", "01-12-20", SSx$SS_date)
SSx$SS_date <- format(as.Date(SSx$SS_date, format="%d-%m-%Y"),"%Y-%m-%d")
SSx$SS_date <- as.Date(SSx$SS_date)
SS <- SSx
rm(SSx)
SS <- SS %>% mutate(start=as.IDate(SS_date))
SS <- SS %>% mutate(end=as.IDate(SS_date + months(1) - days(1)))
SS <- as.data.table(SS)
str(SS)
## Classes 'data.table' and 'data.frame': 732024 obs. of 5 variables:
## $ ID : num 10200257 10200257 10200257 10200257 10200257 ...
## $ SS_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 0.1 0 0.2 0 0 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
SS_5yrWtedAvg <- intervalaverage(x=SS,
y=CARE_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
SS_5yrWtedAvgx <- SS_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
SS_5yrWtedAvgx <- SS_5yrWtedAvgx %>% rename("SS"="value", "start_5yr"="start", "end_5yr"="end")
SS_5yrWtedAvgx <- SS_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(SS_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 160215 obs. of 4 variables:
## $ ID : num 10100001 10100001 10100001 10100001 10100001 ...
## $ SS : num 0.15 0.148 0.145 0.144 0.144 ...
## $ start_5yr: Date, format: "2006-02-20" "2006-03-22" ...
## $ end_5yr : Date, format: "2011-02-20" "2011-03-22" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to CARE
CARE <- left_join(CARE, SS_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
Here I am importing the file which contains monthly Soil level estimates by satellite at nearest lon/lat to CARE patient residential addresses. These are linked to the patient ID.
outfile1 <- here("CARE_fILD_2000_2017_Soil_2021_11_05.xlsx")
Soil <- read_excel(outfile1)
Soil <- Soil %>% dplyr::select(!c(nrow, dist, lon, lat))
Soil <- Soil %>%
pivot_longer(cols=c(2:217), names_to="Soil_date", names_prefix="soil_")
Soilx <- Soil
Soilx$Soil_date <- gsub("jan", "01-01-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("feb", "01-02-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("mar", "01-03-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("apr", "01-04-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("may", "01-05-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("jun", "01-06-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("jul", "01-07-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("aug", "01-08-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("sep", "01-09-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("oct", "01-10-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("nov", "01-11-20", Soilx$Soil_date)
Soilx$Soil_date <- gsub("dec", "01-12-20", Soilx$Soil_date)
Soilx$Soil_date <- format(as.Date(Soilx$Soil_date, format="%d-%m-%Y"),"%Y-%m-%d")
Soilx$Soil_date <- as.Date(Soilx$Soil_date)
Soil <- Soilx
rm(Soilx)
Soil <- Soil %>% mutate(start=as.IDate(Soil_date))
Soil <- Soil %>% mutate(end=as.IDate(Soil_date + months(1) - days(1)))
Soil <- as.data.table(Soil)
str(Soil)
## Classes 'data.table' and 'data.frame': 732024 obs. of 5 variables:
## $ ID : num 10200257 10200257 10200257 10200257 10200257 ...
## $ Soil_date: Date, format: "2000-01-01" "2000-02-01" ...
## $ value : num 0.3 0.2 0 0.5 0.3 ...
## $ start : IDate, format: "2000-01-01" "2000-02-01" ...
## $ end : IDate, format: "2000-01-31" "2000-02-29" ...
## - attr(*, ".internal.selfref")=<externalptr>
Soil_5yrWtedAvg <- intervalaverage(x=Soil,
y=CARE_intervals,
interval_vars=c("start","end"),
value_vars=c("value"),
group_vars="ID",
required_percentage = 0.01)
So this produces a row for each interval for each patient
Select only the necessary columns, but will keep the original dataframe to interrogate data missingess if needed
Soil_5yrWtedAvgx <- Soil_5yrWtedAvg %>% dplyr::select(ID, value, start, end)
Soil_5yrWtedAvgx <- Soil_5yrWtedAvgx %>% rename("Soil"="value", "start_5yr"="start", "end_5yr"="end")
Soil_5yrWtedAvgx <- Soil_5yrWtedAvgx %>% mutate_at(c("start_5yr", "end_5yr"), as.Date)
str(Soil_5yrWtedAvgx)
## Classes 'data.table' and 'data.frame': 160215 obs. of 4 variables:
## $ ID : num 10100001 10100001 10100001 10100001 10100001 ...
## $ Soil : num 0.26 0.259 0.257 0.254 0.249 ...
## $ start_5yr: Date, format: "2006-02-20" "2006-03-22" ...
## $ end_5yr : Date, format: "2011-02-20" "2011-03-22" ...
## - attr(*, ".internal.selfref")=<externalptr>
## - attr(*, "sorted")= chr [1:3] "ID" "start_5yr" "end_5yr"
Join to CARE
CARE <- left_join(CARE, Soil_5yrWtedAvgx, by=c("ID", "start_5yr", "end_5yr"))
rm(CARE_intervals, intervals, PFF_intervals, PM, PM_5yrWtedAvg, PM_5yrWtedAvgx, PM25, Simm_intervals, end, start, IDs, SO4, SO4_5yrWtedAvg, SO4_5yrWtedAvgx, NO3, NO3_5yrWtedAvg, NO3_5yrWtedAvgx, NH4, NH4_5yrWtedAvg, NH4_5yrWtedAvgx, BC, BC_5yrWtedAvg, BC_5yrWtedAvgx, OM, OM_5yrWtedAvg, OM_5yrWtedAvgx, SS, SS_5yrWtedAvg, SS_5yrWtedAvgx, Soil, Soil_5yrWtedAvg, Soil_5yrWtedAvgx)
colnames(Simm)
## [1] "ID" "start" "end"
## [4] "dob" "ADI_nat" "death_date"
## [7] "last_updated" "tx_date" "dx_date"
## [10] "consent_date" "pft_date" "fvc_pct"
## [13] "dlco_pct" "status" "age_dx"
## [16] "time_censoring" "time_death" "time_tx"
## [19] "time_deathORtx" "time_DeathTxCensor" "sex"
## [22] "race" "died" "txed"
## [25] "deadORtx" "dx" "dich_Race"
## [28] "dx_group" "dx_yrmo" "dx_yr"
## [31] "DeathTxCensor_date" "censor_yrmo" "smokeHx"
## [34] "dich_smoking" "dx_IPF" "disadv"
## [37] "days_DeathTxCensor" "event" "end_5yr"
## [40] "start_5yr" "PM" "SO4"
## [43] "NO3" "NH4" "BC"
## [46] "OM" "SS" "Soil"
colnames(PFF)
## [1] "ID" "start" "end"
## [4] "site" "age_dx" "sex"
## [7] "race" "dich_Race" "pct_belowpoverty"
## [10] "dx_group" "dx" "dx_date"
## [13] "death_date" "tx_date" "DeathTxCensor_date"
## [16] "censor_date" "fvc_date" "dlco_date"
## [19] "fvc_pct" "dlco_pct" "status"
## [22] "deadORtx" "time_DeathTxCensor" "dx_yr"
## [25] "smokeHx" "dx_IPF" "disadv"
## [28] "days_DeathTxCensor" "event" "end_5yr"
## [31] "start_5yr" "PM" "SO4"
## [34] "NO3" "NH4" "BC"
## [37] "OM" "SS" "Soil"
colnames(CARE)
## [1] "ID" "start" "end"
## [4] "site" "age_dx" "sex"
## [7] "race" "dich_Race" "avg_s"
## [10] "dx_group" "dx" "dob"
## [13] "dx_date" "death_date" "tx_date"
## [16] "DeathTxCensor_date" "last_updated" "pft_date"
## [19] "fvc_pct" "dlco_pct" "pft_timefromdx"
## [22] "status" "died" "txed"
## [25] "deadORtx" "time_DeathTxCensor" "dx_yr"
## [28] "smokeHx" "dich_smoking" "dx_IPF"
## [31] "disadv" "days_DeathTxCensor" "event"
## [34] "end_5yr" "start_5yr" "PM"
## [37] "SO4" "NO3" "NH4"
## [40] "BC" "OM" "SS"
## [43] "Soil"
Simm <- Simm %>% mutate(site=1)
Simm <- Simm %>% mutate(dlco_date=pft_date)
Simm <- Simm %>% rename(c("fvc_date"="pft_date"))
Simm <- Simm %>% dplyr::select(ID, start, end, site, age_dx, sex, race, dich_Race, smokeHx, dx_IPF, disadv, dx_group, dx, dx_yr, dx_date, death_date, tx_date, DeathTxCensor_date, last_updated, fvc_date, dlco_date, fvc_pct, dlco_pct, status, deadORtx, time_DeathTxCensor, days_DeathTxCensor, event, start_5yr, end_5yr, PM, SO4, NO3, NH4, BC, OM, SS, Soil)
PFF <- PFF %>% rename(c("last_updated"="censor_date"))
PFF <- PFF %>% dplyr::select(ID, start, end, site, age_dx, sex, race, dich_Race, smokeHx, dx_IPF, disadv, dx_group, dx, dx_yr, dx_date, death_date, tx_date, DeathTxCensor_date, last_updated, fvc_date, dlco_date, fvc_pct, dlco_pct, status, deadORtx, time_DeathTxCensor, days_DeathTxCensor, event, start_5yr, end_5yr, PM, SO4, NO3, NH4, BC, OM, SS, Soil)
CARE <- CARE %>% mutate(dlco_date=pft_date)
CARE <- CARE %>% rename(c("fvc_date"="pft_date"))
CARE <- CARE %>% dplyr::select(ID, start, end, site, age_dx, sex, race, dich_Race, smokeHx, dx_IPF, disadv, dx_group, dx, dx_yr, dx_date, death_date, tx_date, DeathTxCensor_date, last_updated, fvc_date, dlco_date, fvc_pct, dlco_pct, status, deadORtx, time_DeathTxCensor, days_DeathTxCensor, event, start_5yr, end_5yr, PM, SO4, NO3, NH4, BC, OM, SS, Soil)
colnames(Simm)==colnames(PFF)
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
colnames(PFF)==colnames(CARE)
## [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
Simm <- Simm %>% mutate(cohort="Simmons")
PFF <- PFF %>% mutate(cohort="PFF")
CARE <- CARE %>% mutate(cohort="CARE-PF")
Fixing sex
PFF <- PFF %>% mutate(sex1=if_else(sex=="Male", "M", "F"))
PFF <- PFF %>% dplyr::select(!sex)
PFF <- PFF %>% rename(c("sex"="sex1"))
PFF <- PFF %>% dplyr::select(ID, start, end, site, age_dx, sex, race, dich_Race, smokeHx, dx_IPF, disadv, dx_group, dx, dx_yr, dx_date, death_date, tx_date, DeathTxCensor_date, last_updated, fvc_date, dlco_date, fvc_pct, dlco_pct, status, deadORtx, time_DeathTxCensor, days_DeathTxCensor, event, start_5yr, end_5yr, PM, SO4, NO3, NH4, BC, OM, SS, Soil, cohort)
Fixing ID - some of the IDs are shared between PFF and Simm
#First want to check how many IDs are shared between Simm and PFF, Simm and CARE, and PFF and CARE
intersect(Simm$ID, PFF$ID)
## [1] 1097 1405
intersect(Simm$ID, CARE$ID)
## numeric(0)
intersect(CARE$ID, PFF$ID)
## numeric(0)
PFF$ID <- paste0(2000, PFF$ID)
intersect(Simm$ID, PFF$ID)
## character(0)
No overlaps between Simm/CARE or PFF/CARE, but two with Simm/PFF, so add “2000” to front of PFF IDs
All <- rbind(Simm, PFF)
## Warning in `[<-.factor`(`*tmp*`, ri, value = c(0, 0, 0, 0, 0, 0, 0, 0, 0, :
## invalid factor level, NA generated
All <- rbind(All, CARE)
str(All)
## 'data.frame': 337348 obs. of 39 variables:
## $ ID : chr "1097" "1097" "1097" "1097" ...
## $ start : num 1 31 61 91 121 151 181 211 241 271 ...
## $ end : num 30 60 90 120 150 180 210 240 270 300 ...
## $ site : chr "1" "1" "1" "1" ...
## $ age_dx : num 64.4 64.4 64.4 64.4 64.4 ...
## $ sex : Factor w/ 2 levels "M","F": 2 2 2 2 2 2 2 2 2 2 ...
## $ race : Factor w/ 7 levels "W","B","A","N",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ dich_Race : Factor w/ 2 levels "White","Non-White": 2 2 2 2 2 2 2 2 2 2 ...
## $ smokeHx : Factor w/ 5 levels "Never","Former",..: 4 4 4 4 4 4 4 4 4 4 ...
## $ dx_IPF : Factor w/ 2 levels "IPF","not_IPF": 2 2 2 2 2 2 2 2 2 2 ...
## $ disadv : num 0.579 0.579 0.579 0.579 0.579 ...
## $ dx_group : Factor w/ 7 levels "IPF","CTD-ILD",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ dx : Factor w/ 39 levels "IPF","AIP","AMYLOIDOSIS",..: 30 30 30 30 30 30 30 30 30 30 ...
## $ dx_yr : num 2002 2002 2002 2002 2002 ...
## $ dx_date : Date, format: "2002-01-24" "2002-01-24" ...
## $ death_date : Date, format: "2008-12-09" "2008-12-09" ...
## $ tx_date : Date, format: NA NA ...
## $ DeathTxCensor_date: Date, format: "2008-12-09" "2008-12-09" ...
## $ last_updated : Date, format: "2021-01-27" "2021-01-27" ...
## $ fvc_date : Date, format: "2002-01-18" "2002-01-18" ...
## $ dlco_date : Date, format: "2002-01-18" "2002-01-18" ...
## $ fvc_pct : num 31.9 31.9 31.9 31.9 31.9 ...
## $ dlco_pct : num NA NA NA NA NA NA NA NA NA NA ...
## $ status : Factor w/ 3 levels "0","1","2": 2 2 2 2 2 2 2 2 2 2 ...
## $ deadORtx : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ time_DeathTxCensor: num 6.87 6.87 6.87 6.87 6.87 ...
## $ days_DeathTxCensor: num 2511 2511 2511 2511 2511 ...
## $ event : num 0 0 0 0 0 0 0 0 0 0 ...
## $ start_5yr : Date, format: "1997-02-23" "1997-03-25" ...
## $ end_5yr : Date, format: "2002-02-23" "2002-03-25" ...
## $ PM : num 12.5 12.4 12.3 12.3 12.5 ...
## $ SO4 : num 4.41 4.37 4.35 4.33 4.41 ...
## $ NO3 : num 1.4 1.41 1.4 1.38 1.36 ...
## $ NH4 : num 1.7 1.69 1.68 1.67 1.69 ...
## $ BC : num 0.774 0.768 0.762 0.753 0.763 ...
## $ OM : num 2.9 2.89 2.88 2.85 2.9 ...
## $ SS : num 0.241 0.248 0.242 0.248 0.249 ...
## $ Soil : num 0.521 0.513 0.511 0.511 0.515 ...
## $ cohort : chr "Simmons" "Simmons" "Simmons" "Simmons" ...
All$cohort <- as.factor(All$cohort)
All$site <- as.factor(All$site)
All$event <- as.factor(All$event)
write_xlsx(All, path="CombinedCohorts_fILD_AllConstituentsMatched_2023_01_02.xlsx")
Simm_medPM <- Simm %>% group_by(ID) %>% summarise(Mean=mean(PM, na.rm=T), Max=max(PM, na.rm=T), Min=min(PM, na.rm=T), Median=median(PM, na.rm=T), StdDev=sd(PM, na.rm=T), Q1=quantile(PM, 0.25, na.rm=T), Q3=quantile(PM, 0.75, na.rm=T))
Simm_medPM %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 10.7 17.3 3.57 10.4 0.559 8.40 13.0
Simm_medSO4 <- Simm %>% group_by(ID) %>% summarise(Mean=mean(SO4, na.rm=T), Max=max(SO4, na.rm=T), Min=min(SO4, na.rm=T), Median=median(SO4, na.rm=T), StdDev=sd(SO4, na.rm=T), Q1=quantile(SO4, 0.25, na.rm=T), Q3=quantile(SO4, 0.75, na.rm=T))
Simm_medSO4 %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3.23 6.70 0.516 2.91 0.353 1.88 4.67
Simm_medNO3 <- Simm %>% group_by(ID) %>% summarise(Mean=mean(NO3, na.rm=T), Max=max(NO3, na.rm=T), Min=min(NO3, na.rm=T), Median=median(NO3, na.rm=T), StdDev=sd(NO3, na.rm=T), Q1=quantile(NO3, 0.25, na.rm=T), Q3=quantile(NO3, 0.75, na.rm=T))
Simm_medNO3 %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.05 3.92 0.211 1.02 0.0518 0.825 1.25
Simm_medNH4 <- Simm %>% group_by(ID) %>% summarise(Mean=mean(NH4, na.rm=T), Max=max(NH4, na.rm=T), Min=min(NH4, na.rm=T), Median=median(NH4, na.rm=T), StdDev=sd(NH4, na.rm=T), Q1=quantile(NH4, 0.25, na.rm=T), Q3=quantile(NH4, 0.75, na.rm=T))
Simm_medNH4 %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.21 2.61 0.0361 1.14 0.145 0.663 1.77
Simm_medBC <- Simm %>% group_by(ID) %>% summarise(Mean=mean(BC, na.rm=T), Max=max(BC, na.rm=T), Min=min(BC, na.rm=T), Median=median(BC, na.rm=T), StdDev=sd(BC, na.rm=T), Q1=quantile(BC, 0.25, na.rm=T), Q3=quantile(BC, 0.75, na.rm=T))
Simm_medBC %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.842 1.88 0.240 0.829 0.0373 0.686 1.00
Simm_medOM <- Simm %>% group_by(ID) %>% summarise(Mean=mean(OM, na.rm=T), Max=max(OM, na.rm=T), Min=min(OM, na.rm=T), Median=median(OM, na.rm=T), StdDev=sd(OM, na.rm=T), Q1=quantile(OM, 0.25, na.rm=T), Q3=quantile(OM, 0.75, na.rm=T))
Simm_medOM %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3.22 6.81 1.11 3.16 0.107 2.67 3.66
Simm_medSS <- Simm %>% group_by(ID) %>% summarise(Mean=mean(SS, na.rm=T), Max=max(SS, na.rm=T), Min=min(SS, na.rm=T), Median=median(SS, na.rm=T), StdDev=sd(SS, na.rm=T), Q1=quantile(SS, 0.25, na.rm=T), Q3=quantile(SS, 0.75, na.rm=T))
Simm_medSS %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.206 1.51 0 0.176 0.0233 0.128 0.271
Simm_medSoil <- Simm %>% group_by(ID) %>% summarise(Mean=mean(Soil, na.rm=T), Max=max(Soil, na.rm=T), Min=min(Soil, na.rm=T), Median=median(Soil, na.rm=T), StdDev=sd(Soil, na.rm=T), Q1=quantile(Soil, 0.25, na.rm=T), Q3=quantile(Soil, 0.75, na.rm=T))
Simm_medSoil %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.482 1.36 0.123 0.487 0.0172 0.369 0.558
PFF_medPM <- PFF %>% group_by(ID) %>% summarise(Mean=mean(PM, na.rm=T), Max=max(PM, na.rm=T), Min=min(PM, na.rm=T), Median=median(PM, na.rm=T), StdDev=sd(PM, na.rm=T), Q1=quantile(PM, 0.25, na.rm=T), Q3=quantile(PM, 0.75, na.rm=T))
## Warning in max(PM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(PM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(PM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(PM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(PM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(PM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(PM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(PM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(PM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(PM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(PM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in min(PM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(PM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(PM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(PM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(PM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(PM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(PM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(PM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(PM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(PM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(PM, na.rm = T): no non-missing arguments to min; returning Inf
PFF_medPM %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 8.61 17.3 2.47 8.55 0.203 7.33 9.88
PFF_medPM <- inner_join(PFF_medPM, PFF, by="ID")
PFF_medPM %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 41 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01R 6.15 10.4 3.84 5.91 0.104 5.21 6.71
## 2 02R 8.09 12.6 3.70 8.12 0.159 6.53 10.0
## 3 03R 6.09 9.37 2.53 6.41 0.110 4.16 7.79
## 4 04R 10.4 16.4 6.91 9.64 0.139 9.05 11.4
## 5 05R 10.8 15.0 8.05 10.6 0.133 9.61 11.8
## 6 06R 9.35 14.2 4.37 9.34 0.217 8.17 10.4
## 7 07R 8.61 12.0 4.54 8.54 0.177 7.74 9.70
## 8 09R 8.03 14.8 5.30 7.63 0.152 6.56 8.92
## 9 10R 7.29 8.63 6.15 7.24 0.0666 6.96 7.69
## 10 11R 9.38 15.8 7.06 9.14 0.219 8.43 10.1
## # … with 31 more rows
PFF_medSO4 <- PFF %>% group_by(ID) %>% summarise(Mean=mean(SO4, na.rm=T), Max=max(SO4, na.rm=T), Min=min(SO4, na.rm=T), Median=median(SO4, na.rm=T), StdDev=sd(SO4, na.rm=T), Q1=quantile(SO4, 0.25, na.rm=T), Q3=quantile(SO4, 0.75, na.rm=T))
## Warning in max(SO4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SO4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SO4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SO4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SO4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SO4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SO4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SO4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SO4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SO4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SO4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in min(SO4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SO4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SO4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SO4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SO4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SO4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SO4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SO4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SO4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SO4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SO4, na.rm = T): no non-missing arguments to min; returning Inf
PFF_medSO4 %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.59 5.16 0.236 1.55 0.119 1.12 2.06
PFF_medSO4 <- inner_join(PFF_medSO4, PFF, by="ID")
PFF_medSO4 %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 41 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01R 0.496 1.18 0.265 0.466 0.0204 0.360 0.566
## 2 02R 0.671 1.09 0.290 0.708 0.0142 0.574 0.767
## 3 03R 0.643 1.51 0.248 0.618 0.0260 0.487 0.815
## 4 04R 1.94 3.96 1.13 1.62 0.0614 1.42 2.22
## 5 05R 2.64 4.93 1.37 2.50 0.106 1.88 3.57
## 6 06R 2.24 5.03 0.749 1.99 0.134 1.70 2.57
## 7 07R 1.65 3.44 0.729 1.56 0.0639 1.31 1.97
## 8 09R 1.47 4.56 0.723 1.22 0.103 1.02 1.69
## 9 10R 1.16 1.98 0.760 1.04 0.0675 0.897 1.43
## 10 11R 1.90 5.16 1.09 1.79 0.108 1.51 2.18
## # … with 31 more rows
PFF_medNO3 <- PFF %>% group_by(ID) %>% summarise(Mean=mean(NO3, na.rm=T), Max=max(NO3, na.rm=T), Min=min(NO3, na.rm=T), Median=median(NO3, na.rm=T), StdDev=sd(NO3, na.rm=T), Q1=quantile(NO3, 0.25, na.rm=T), Q3=quantile(NO3, 0.75, na.rm=T))
## Warning in max(NO3, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NO3, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NO3, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NO3, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NO3, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NO3, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NO3, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NO3, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NO3, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NO3, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NO3, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in min(NO3, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NO3, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NO3, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NO3, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NO3, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NO3, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NO3, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NO3, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NO3, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NO3, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NO3, na.rm = T): no non-missing arguments to min; returning Inf
PFF_medNO3 %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.987 4.76 0.0287 0.889 0.0476 0.505 1.42
PFF_medNO3 <- inner_join(PFF_medNO3, PFF, by="ID")
PFF_medNO3 %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 41 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01R 0.405 0.763 0.0715 0.411 0.00859 0.323 0.522
## 2 02R 1.10 2.88 0.112 1.13 0.0279 0.736 1.41
## 3 03R 0.755 1.74 0.0734 0.675 0.0316 0.272 1.30
## 4 04R 1.83 3.60 0.372 1.80 0.0432 1.62 2.09
## 5 05R 1.26 1.81 0.697 1.25 0.0303 1.10 1.38
## 6 06R 0.748 1.76 0.134 0.702 0.0232 0.529 0.925
## 7 07R 1.45 2.06 0.345 1.44 0.0289 1.31 1.66
## 8 09R 0.858 2.29 0.478 0.703 0.0199 0.608 1.05
## 9 10R 0.340 0.542 0.255 0.334 0.00550 0.303 0.359
## 10 11R 0.416 0.778 0.220 0.438 0.0111 0.312 0.507
## # … with 31 more rows
PFF_medNH4 <- PFF %>% group_by(ID) %>% summarise(Mean=mean(NH4, na.rm=T), Max=max(NH4, na.rm=T), Min=min(NH4, na.rm=T), Median=median(NH4, na.rm=T), StdDev=sd(NH4, na.rm=T), Q1=quantile(NH4, 0.25, na.rm=T), Q3=quantile(NH4, 0.75, na.rm=T))
## Warning in max(NH4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NH4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NH4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NH4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NH4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NH4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NH4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NH4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NH4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NH4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(NH4, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in min(NH4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NH4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NH4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NH4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NH4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NH4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NH4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NH4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NH4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NH4, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(NH4, na.rm = T): no non-missing arguments to min; returning Inf
PFF_medNH4 %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.574 2.48 0.0145 0.510 0.0610 0.309 0.806
PFF_medNH4 <- inner_join(PFF_medNH4, PFF, by="ID")
PFF_medNH4 %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 41 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01R 0.213 0.652 0.0420 0.158 0.0161 0.112 0.312
## 2 02R 0.338 1.20 0.0394 0.303 0.0121 0.220 0.483
## 3 03R 0.277 0.635 0.0145 0.280 0.0168 0.0982 0.425
## 4 04R 0.934 2.17 0.0856 0.721 0.0485 0.566 1.25
## 5 05R 1.01 2.04 0.368 0.899 0.0538 0.650 1.47
## 6 06R 0.711 2.02 0.195 0.597 0.0565 0.434 0.917
## 7 07R 0.721 1.72 0.115 0.686 0.0382 0.507 0.901
## 8 09R 0.562 2.13 0.134 0.433 0.0467 0.296 0.671
## 9 10R 0.130 0.439 0.0347 0.0903 0.0168 0.0587 0.184
## 10 11R 0.398 1.55 0.0905 0.364 0.0434 0.266 0.509
## # … with 31 more rows
PFF_medBC <- PFF %>% group_by(ID) %>% summarise(Mean=mean(BC, na.rm=T), Max=max(BC, na.rm=T), Min=min(BC, na.rm=T), Median=median(BC, na.rm=T), StdDev=sd(BC, na.rm=T), Q1=quantile(BC, 0.25, na.rm=T), Q3=quantile(BC, 0.75, na.rm=T))
## Warning in max(BC, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(BC, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(BC, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(BC, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(BC, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(BC, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(BC, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(BC, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(BC, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(BC, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(BC, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in min(BC, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(BC, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(BC, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(BC, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(BC, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(BC, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(BC, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(BC, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(BC, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(BC, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(BC, na.rm = T): no non-missing arguments to min; returning Inf
PFF_medBC %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.657 2.54 0.0726 0.640 0.0378 0.498 0.806
PFF_medBC <- inner_join(PFF_medBC, PFF, by="ID")
PFF_medBC %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 41 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01R 0.464 1.07 0.214 0.383 0.0257 0.308 0.583
## 2 02R 0.517 0.943 0.161 0.521 0.0168 0.372 0.679
## 3 03R 0.603 1.22 0.133 0.489 0.0203 0.232 0.988
## 4 04R 0.729 1.09 0.508 0.696 0.0102 0.619 0.803
## 5 05R 0.809 1.28 0.588 0.778 0.0212 0.672 0.889
## 6 06R 0.749 1.11 0.344 0.745 0.0287 0.627 0.910
## 7 07R 0.610 0.940 0.317 0.587 0.0191 0.533 0.711
## 8 09R 0.622 1.46 0.335 0.480 0.0484 0.419 0.776
## 9 10R 0.378 0.544 0.280 0.348 0.0186 0.314 0.455
## 10 11R 0.831 1.65 0.533 0.809 0.0408 0.699 0.917
## # … with 31 more rows
PFF_medOM <- PFF %>% group_by(ID) %>% summarise(Mean=mean(OM, na.rm=T), Max=max(OM, na.rm=T), Min=min(OM, na.rm=T), Median=median(OM, na.rm=T), StdDev=sd(OM, na.rm=T), Q1=quantile(OM, 0.25, na.rm=T), Q3=quantile(OM, 0.75, na.rm=T))
## Warning in max(OM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(OM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(OM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(OM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(OM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(OM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(OM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(OM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(OM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(OM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(OM, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in min(OM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(OM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(OM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(OM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(OM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(OM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(OM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(OM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(OM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(OM, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(OM, na.rm = T): no non-missing arguments to min; returning Inf
PFF_medOM %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2.92 8.32 0.504 2.78 0.0912 2.26 3.51
PFF_medOM <- inner_join(PFF_medOM, PFF, by="ID")
PFF_medOM %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 41 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01R 2.55 5.66 1.12 2.03 0.0806 1.52 3.75
## 2 02R 2.93 5.54 0.936 3.00 0.107 2.05 3.82
## 3 03R 3.14 7.28 0.695 2.71 0.199 1.10 5.02
## 4 04R 2.74 4.03 2.25 2.60 0.0363 2.47 2.98
## 5 05R 3.39 5.08 2.45 3.06 0.0371 2.71 4.23
## 6 06R 3.25 5.09 1.51 3.17 0.0464 2.81 3.56
## 7 07R 2.88 4.35 1.55 2.73 0.0667 2.43 3.31
## 8 09R 2.87 6.02 1.69 2.21 0.0782 1.87 4.01
## 9 10R 1.63 2.87 1.14 1.51 0.0398 1.30 1.85
## 10 11R 3.98 6.44 2.97 3.99 0.0740 3.58 4.35
## # … with 31 more rows
PFF_medSS <- PFF %>% group_by(ID) %>% summarise(Mean=mean(SS, na.rm=T), Max=max(SS, na.rm=T), Min=min(SS, na.rm=T), Median=median(SS, na.rm=T), StdDev=sd(SS, na.rm=T), Q1=quantile(SS, 0.25, na.rm=T), Q3=quantile(SS, 0.75, na.rm=T))
## Warning in max(SS, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SS, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SS, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SS, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SS, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SS, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SS, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SS, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SS, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SS, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(SS, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in min(SS, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SS, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SS, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SS, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SS, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SS, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SS, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SS, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SS, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SS, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(SS, na.rm = T): no non-missing arguments to min; returning Inf
PFF_medSS %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.371 2.71 0 0.245 0.0216 0.173 0.416
PFF_medSS <- inner_join(PFF_medSS, PFF, by="ID")
PFF_medSS %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 41 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01R 0.314 1.23 0.0742 0.241 0.0290 0.170 0.299
## 2 02R 1.47 2.71 0.0531 1.56 0.0291 0.799 2.13
## 3 03R 0.168 1.57 0 0.112 0.0294 0.0185 0.207
## 4 04R 0.264 0.835 0.110 0.228 0.0155 0.191 0.266
## 5 05R 0.131 0.230 0.0703 0.122 0.00643 0.0911 0.173
## 6 06R 0.135 0.332 0.0696 0.124 0.00785 0.0994 0.160
## 7 07R 0.243 0.877 0.138 0.221 0.0120 0.189 0.267
## 8 09R 0.367 0.611 0.189 0.353 0.0129 0.284 0.425
## 9 10R 1.08 1.66 0.593 1.06 0.0344 0.925 1.25
## 10 11R 0.268 0.688 0.127 0.236 0.00976 0.206 0.289
## # … with 31 more rows
PFF_medSoil <- PFF %>% group_by(ID) %>% summarise(Mean=mean(Soil, na.rm=T), Max=max(Soil, na.rm=T), Min=min(Soil, na.rm=T), Median=median(Soil, na.rm=T), StdDev=sd(Soil, na.rm=T), Q1=quantile(Soil, 0.25, na.rm=T), Q3=quantile(Soil, 0.75, na.rm=T))
## Warning in max(Soil, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(Soil, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(Soil, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(Soil, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(Soil, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(Soil, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(Soil, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(Soil, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(Soil, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(Soil, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in max(Soil, na.rm = T): no non-missing arguments to max; returning -Inf
## Warning in min(Soil, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(Soil, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(Soil, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(Soil, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(Soil, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(Soil, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(Soil, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(Soil, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(Soil, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(Soil, na.rm = T): no non-missing arguments to min; returning Inf
## Warning in min(Soil, na.rm = T): no non-missing arguments to min; returning Inf
PFF_medSoil %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.616 2.85 0.0621 0.494 0.0281 0.337 0.770
PFF_medSoil <- inner_join(PFF_medSoil, PFF, by="ID")
PFF_medSoil %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 41 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 01R 0.256 0.644 0.0829 0.200 0.0148 0.126 0.379
## 2 02R 0.405 1.01 0.0989 0.402 0.00942 0.290 0.508
## 3 03R 0.923 1.59 0.0839 0.844 0.0215 0.542 1.35
## 4 04R 0.541 0.801 0.401 0.521 0.00716 0.484 0.578
## 5 05R 0.515 0.704 0.277 0.487 0.00740 0.429 0.649
## 6 06R 0.542 0.910 0.0686 0.533 0.0111 0.476 0.639
## 7 07R 0.525 1.18 0.188 0.440 0.0223 0.351 0.641
## 8 09R 0.282 0.719 0.0994 0.206 0.00833 0.155 0.403
## 9 10R 1.04 1.42 0.578 1.05 0.0290 0.871 1.20
## 10 11R 0.770 1.11 0.501 0.758 0.0126 0.691 0.835
## # … with 31 more rows
CARE_medPM <- CARE %>% group_by(ID) %>% summarise(Mean=mean(PM, na.rm=T), Max=max(PM, na.rm=T), Min=min(PM, na.rm=T), Median=median(PM, na.rm=T), StdDev=sd(PM, na.rm=T), Q1=quantile(PM, 0.25, na.rm=T), Q3=quantile(PM, 0.75, na.rm=T))
CARE_medPM %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 6.37 43.8 1.28 6.17 0.442 5.04 7.94
CARE_medPM <- inner_join(CARE_medPM, CARE, by="ID")
CARE_medPM %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 8 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 101 7.10 9.32 2.50 7.52 0.152 6.35 8.22
## 2 102 5.27 9.34 2.65 4.96 0.153 4.09 6.65
## 3 103 5.43 43.8 1.28 5.69 0.668 4.61 6.44
## 4 104 5.64 19.0 1.35 5.76 0.334 5.09 6.48
## 5 105 7.78 11.2 2.31 8.12 0.365 6.49 9.62
## 6 106 8.38 11.7 2.77 8.34 0.284 7.70 9.21
## 7 107 7.48 12.2 2.22 7.47 0.278 6.25 8.88
## 8 108 5.52 6.98 3.50 5.57 0.0786 5.08 5.94
CARE_medSO4 <- CARE %>% group_by(ID) %>% summarise(Mean=mean(SO4, na.rm=T), Max=max(SO4, na.rm=T), Min=min(SO4, na.rm=T), Median=median(SO4, na.rm=T), StdDev=sd(SO4, na.rm=T), Q1=quantile(SO4, 0.25, na.rm=T), Q3=quantile(SO4, 0.75, na.rm=T))
CARE_medSO4 %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.820 6.46 0.135 0.611 0.0967 0.414 1.28
CARE_medSO4 <- inner_join(CARE_medSO4, CARE, by="ID")
CARE_medSO4 %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 8 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 101 1.28 2.92 0.354 1.32 0.111 1.07 1.58
## 2 102 0.454 1.12 0.218 0.435 0.0243 0.376 0.535
## 3 103 0.514 6.46 0.135 0.499 0.0432 0.368 0.671
## 4 104 0.518 6.32 0.188 0.501 0.103 0.382 0.686
## 5 105 1.21 2.69 0.377 1.15 0.131 0.933 1.64
## 6 106 1.64 3.95 0.417 1.56 0.193 1.32 1.94
## 7 107 1.14 2.67 0.342 1.04 0.143 0.903 1.28
## 8 108 0.553 0.767 0.333 0.570 0.00559 0.486 0.600
CARE_medNO3 <- CARE %>% group_by(ID) %>% summarise(Mean=mean(NO3, na.rm=T), Max=max(NO3, na.rm=T), Min=min(NO3, na.rm=T), Median=median(NO3, na.rm=T), StdDev=sd(NO3, na.rm=T), Q1=quantile(NO3, 0.25, na.rm=T), Q3=quantile(NO3, 0.75, na.rm=T))
CARE_medNO3 %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.626 2.14 0.0134 0.548 0.0377 0.345 0.968
CARE_medNO3 <- inner_join(CARE_medNO3, CARE, by="ID")
CARE_medNO3 %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 8 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 101 0.910 1.64 0.173 1.02 0.0203 0.809 1.08
## 2 102 0.588 1.97 0.0798 0.493 0.0426 0.327 0.890
## 3 103 0.368 1.45 0.0134 0.383 0.0282 0.228 0.506
## 4 104 0.395 0.909 0.0393 0.419 0.0297 0.293 0.519
## 5 105 0.795 1.68 0.0501 0.834 0.0557 0.613 1.04
## 6 106 1.15 2.14 0.179 1.16 0.0537 1.06 1.25
## 7 107 0.760 1.90 0.0788 0.772 0.0600 0.605 0.922
## 8 108 0.646 1.17 0.137 0.652 0.0130 0.524 0.811
CARE_medNH4 <- CARE %>% group_by(ID) %>% summarise(Mean=mean(NH4, na.rm=T), Max=max(NH4, na.rm=T), Min=min(NH4, na.rm=T), Median=median(NH4, na.rm=T), StdDev=sd(NH4, na.rm=T), Q1=quantile(NH4, 0.25, na.rm=T), Q3=quantile(NH4, 0.75, na.rm=T))
CARE_medNH4 %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.293 1.66 0 0.212 0.0534 0.101 0.511
CARE_medNH4 <- inner_join(CARE_medNH4, CARE, by="ID")
CARE_medNH4 %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 8 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 101 0.496 1.20 0.0290 0.531 0.0495 0.376 0.669
## 2 102 0.203 0.812 0.0455 0.179 0.0305 0.128 0.297
## 3 103 0.146 1.65 0 0.114 0.0340 0.0719 0.224
## 4 104 0.143 1.61 0.0104 0.119 0.0431 0.0700 0.200
## 5 105 0.474 1.40 0.0213 0.440 0.0837 0.300 0.733
## 6 106 0.691 1.66 0 0.652 0.0964 0.528 0.900
## 7 107 0.429 1.31 0.0222 0.381 0.0822 0.287 0.502
## 8 108 0.189 0.341 0.0388 0.205 0.00919 0.135 0.253
CARE_medBC <- CARE %>% group_by(ID) %>% summarise(Mean=mean(BC, na.rm=T), Max=max(BC, na.rm=T), Min=min(BC, na.rm=T), Median=median(BC, na.rm=T), StdDev=sd(BC, na.rm=T), Q1=quantile(BC, 0.25, na.rm=T), Q3=quantile(BC, 0.75, na.rm=T))
CARE_medBC %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.469 1.05 0.0401 0.472 0.0205 0.310 0.633
CARE_medBC <- inner_join(CARE_medBC, CARE, by="ID")
CARE_medBC %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 8 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 101 0.506 0.804 0.164 0.506 0.0163 0.411 0.629
## 2 102 0.391 0.981 0.141 0.317 0.0114 0.221 0.594
## 3 103 0.439 1.05 0.0401 0.376 0.0210 0.242 0.689
## 4 104 0.473 0.918 0.0648 0.416 0.0171 0.286 0.699
## 5 105 0.514 0.982 0.147 0.523 0.0384 0.409 0.652
## 6 106 0.613 0.977 0.215 0.581 0.0273 0.509 0.728
## 7 107 0.496 0.900 0.156 0.477 0.0288 0.390 0.562
## 8 108 0.341 0.517 0.176 0.341 0.00426 0.287 0.389
CARE_medOM <- CARE %>% group_by(ID) %>% summarise(Mean=mean(OM, na.rm=T), Max=max(OM, na.rm=T), Min=min(OM, na.rm=T), Median=median(OM, na.rm=T), StdDev=sd(OM, na.rm=T), Q1=quantile(OM, 0.25, na.rm=T), Q3=quantile(OM, 0.75, na.rm=T))
CARE_medOM %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2.97 10.8 0.126 2.75 0.179 1.94 4.20
CARE_medOM <- inner_join(CARE_medOM, CARE, by="ID")
CARE_medOM %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 8 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 101 2.72 4.87 0.799 2.66 0.0987 2.04 3.61
## 2 102 2.64 7.32 1.00 2.07 0.117 1.33 4.06
## 3 103 2.92 10.8 0.126 2.41 0.269 1.50 4.58
## 4 104 3.18 6.91 0.178 2.82 0.223 1.85 4.60
## 5 105 3.66 7.91 0.890 3.70 0.282 2.65 4.92
## 6 106 3.18 5.80 1.15 2.83 0.132 2.38 4.15
## 7 107 3.54 6.50 0.770 3.42 0.267 2.64 4.42
## 8 108 2.25 3.97 1.34 2.24 0.0945 1.82 2.75
CARE_medSS <- CARE %>% group_by(ID) %>% summarise(Mean=mean(SS, na.rm=T), Max=max(SS, na.rm=T), Min=min(SS, na.rm=T), Median=median(SS, na.rm=T), StdDev=sd(SS, na.rm=T), Q1=quantile(SS, 0.25, na.rm=T), Q3=quantile(SS, 0.75, na.rm=T))
CARE_medSS %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.243 1.48 0.00537 0.248 0.0151 0.186 0.308
CARE_medSS <- inner_join(CARE_medSS, CARE, by="ID")
CARE_medSS %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 8 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 101 0.220 0.334 0.0316 0.228 0.00931 0.173 0.275
## 2 102 0.0688 0.289 0.00537 0.0540 0.00255 0.0231 0.115
## 3 103 0.317 1.48 0.0161 0.316 0.0286 0.237 0.369
## 4 104 0.292 1.12 0.0239 0.307 0.0228 0.224 0.353
## 5 105 0.229 0.314 0.0719 0.237 0.00669 0.207 0.257
## 6 106 0.247 0.400 0.0658 0.246 0.0113 0.196 0.290
## 7 107 0.241 1.10 0.0825 0.243 0.0294 0.220 0.262
## 8 108 0.0596 0.128 0.0165 0.0514 0.00186 0.0377 0.0865
CARE_medSoil <- CARE %>% group_by(ID) %>% summarise(Mean=mean(Soil, na.rm=T), Max=max(Soil, na.rm=T), Min=min(Soil, na.rm=T), Median=median(Soil, na.rm=T), StdDev=sd(Soil, na.rm=T), Q1=quantile(Soil, 0.25, na.rm=T), Q3=quantile(Soil, 0.75, na.rm=T))
CARE_medSoil %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 1 × 7
## Mean Max Min Median StdDev Q1 Q3
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.326 1.15 0.0117 0.313 0.0188 0.189 0.430
CARE_medSoil <- inner_join(CARE_medSoil, CARE, by="ID")
CARE_medSoil %>% group_by(site) %>% summarise(Mean=mean(Mean, na.rm=T), Max=max(Max, na.rm=T), Min=min(Min, na.rm=T), Median=median(Median, na.rm=T), StdDev=sd(StdDev, na.rm=T), Q1=quantile(Q1, 0.25, na.rm=T), Q3=quantile(Q3, 0.75, na.rm=T))
## # A tibble: 8 × 8
## site Mean Max Min Median StdDev Q1 Q3
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 101 0.409 0.809 0.0664 0.417 0.0212 0.266 0.591
## 2 102 0.347 0.705 0.114 0.313 0.00860 0.259 0.460
## 3 103 0.242 1.15 0.0117 0.215 0.0284 0.145 0.350
## 4 104 0.250 1.09 0.0248 0.233 0.0301 0.161 0.346
## 5 105 0.331 0.500 0.0532 0.350 0.00999 0.256 0.421
## 6 106 0.512 0.865 0.0837 0.468 0.0263 0.369 0.700
## 7 107 0.334 0.599 0.0749 0.355 0.0167 0.255 0.429
## 8 108 0.384 0.555 0.203 0.386 0.00486 0.333 0.449
Simm_medPM <- Simm_medPM %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
Simm_medSO4 <- Simm_medSO4 %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
Simm_medNO3 <- Simm_medNO3 %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
Simm_medNH4 <- Simm_medNH4 %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
Simm_medBC <- Simm_medBC %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
Simm_medOM <- Simm_medOM %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
Simm_medSS <- Simm_medSS %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
Simm_medSoil <- Simm_medSoil %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
PFF_medPM <- PFF_medPM %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
PFF_medSO4 <- PFF_medSO4 %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
PFF_medNO3 <- PFF_medNO3 %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
PFF_medNH4 <- PFF_medNH4 %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
PFF_medBC <- PFF_medBC %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
PFF_medOM <- PFF_medOM %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
PFF_medSS <- PFF_medSS %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
PFF_medSoil <- PFF_medSoil %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
CARE_medPM <- CARE_medPM %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
CARE_medSO4 <- CARE_medSO4 %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
CARE_medNO3 <- CARE_medNO3 %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
CARE_medNH4 <- CARE_medNH4 %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
CARE_medBC <- CARE_medBC %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
CARE_medOM <- CARE_medOM %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
CARE_medSS <- CARE_medSS %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
CARE_medSoil <- CARE_medSoil %>% dplyr::select(ID, Mean, Median, Q1, Q3, StdDev)
Simm_medPM <- Simm_medPM %>% mutate(pollutant="PM")
Simm_medSO4 <- Simm_medSO4 %>% mutate(pollutant="SO4")
Simm_medNO3 <- Simm_medNO3 %>% mutate(pollutant="NO3")
Simm_medNH4 <- Simm_medNH4 %>% mutate(pollutant="NH4")
Simm_medBC <- Simm_medBC %>% mutate(pollutant="BC")
Simm_medOM <- Simm_medOM %>% mutate(pollutant="OM")
Simm_medSS <- Simm_medSS %>% mutate(pollutant="SS")
Simm_medSoil <- Simm_medSoil %>% mutate(pollutant="Soil")
PFF_medPM <- PFF_medPM %>% mutate(pollutant="PM")
PFF_medSO4 <- PFF_medSO4 %>% mutate(pollutant="SO4")
PFF_medNO3 <- PFF_medNO3 %>% mutate(pollutant="NO3")
PFF_medNH4 <- PFF_medNH4 %>% mutate(pollutant="NH4")
PFF_medBC <- PFF_medBC %>% mutate(pollutant="BC")
PFF_medOM <- PFF_medOM %>% mutate(pollutant="OM")
PFF_medSS <- PFF_medSS %>% mutate(pollutant="SS")
PFF_medSoil <- PFF_medSoil %>% mutate(pollutant="Soil")
CARE_medPM <- CARE_medPM %>% mutate(pollutant="PM")
CARE_medSO4 <- CARE_medSO4 %>% mutate(pollutant="SO4")
CARE_medNO3 <- CARE_medNO3 %>% mutate(pollutant="NO3")
CARE_medNH4 <- CARE_medNH4 %>% mutate(pollutant="NH4")
CARE_medBC <- CARE_medBC %>% mutate(pollutant="BC")
CARE_medOM <- CARE_medOM %>% mutate(pollutant="OM")
CARE_medSS <- CARE_medSS %>% mutate(pollutant="SS")
CARE_medSoil <- CARE_medSoil %>% mutate(pollutant="Soil")
Simm_medPM <- Simm_medPM %>% mutate(cohort="Simm")
Simm_medSO4 <- Simm_medSO4 %>% mutate(cohort="Simm")
Simm_medNO3 <- Simm_medNO3 %>% mutate(cohort="Simm")
Simm_medNH4 <- Simm_medNH4 %>% mutate(cohort="Simm")
Simm_medBC <- Simm_medBC %>% mutate(cohort="Simm")
Simm_medOM <- Simm_medOM %>% mutate(cohort="Simm")
Simm_medSS <- Simm_medSS %>% mutate(cohort="Simm")
Simm_medSoil <- Simm_medSoil %>% mutate(cohort="Simm")
PFF_medPM <- PFF_medPM %>% mutate(cohort="PFF")
PFF_medSO4 <- PFF_medSO4 %>% mutate(cohort="PFF")
PFF_medNO3 <- PFF_medNO3 %>% mutate(cohort="PFF")
PFF_medNH4 <- PFF_medNH4 %>% mutate(cohort="PFF")
PFF_medBC <- PFF_medBC %>% mutate(cohort="PFF")
PFF_medOM <- PFF_medOM %>% mutate(cohort="PFF")
PFF_medSS <- PFF_medSS %>% mutate(cohort="PFF")
PFF_medSoil <- PFF_medSoil %>% mutate(cohort="PFF")
CARE_medPM <- CARE_medPM %>% mutate(cohort="CARE")
CARE_medSO4 <- CARE_medSO4 %>% mutate(cohort="CARE")
CARE_medNO3 <- CARE_medNO3 %>% mutate(cohort="CARE")
CARE_medNH4 <- CARE_medNH4 %>% mutate(cohort="CARE")
CARE_medBC <- CARE_medBC %>% mutate(cohort="CARE")
CARE_medOM <- CARE_medOM %>% mutate(cohort="CARE")
CARE_medSS <- CARE_medSS %>% mutate(cohort="CARE")
CARE_medSoil <- CARE_medSoil %>% mutate(cohort="CARE")
PFF_medPM <- PFF_medPM %>% group_by(ID) %>% slice(1)
PFF_medSO4 <- PFF_medSO4 %>% group_by(ID) %>% slice(1)
PFF_medNO3 <- PFF_medNO3 %>% group_by(ID) %>% slice(1)
PFF_medNH4 <- PFF_medNH4 %>% group_by(ID) %>% slice(1)
PFF_medBC <- PFF_medBC %>% group_by(ID) %>% slice(1)
PFF_medOM <- PFF_medOM %>% group_by(ID) %>% slice(1)
PFF_medSS <- PFF_medSS %>% group_by(ID) %>% slice(1)
PFF_medSoil <- PFF_medSoil %>% group_by(ID) %>% slice(1)
CARE_medPM <- CARE_medPM %>% group_by(ID) %>% slice(1)
CARE_medSO4 <- CARE_medSO4 %>% group_by(ID) %>% slice(1)
CARE_medNO3 <- CARE_medNO3 %>% group_by(ID) %>% slice(1)
CARE_medNH4 <- CARE_medNH4 %>% group_by(ID) %>% slice(1)
CARE_medBC <- CARE_medBC %>% group_by(ID) %>% slice(1)
CARE_medOM <- CARE_medOM %>% group_by(ID) %>% slice(1)
CARE_medSS <- CARE_medSS %>% group_by(ID) %>% slice(1)
CARE_medSoil <- CARE_medSoil %>% group_by(ID) %>% slice(1)
Simm_summary <- rbind(Simm_medPM, Simm_medSO4, Simm_medNO3, Simm_medNH4, Simm_medBC, Simm_medOM, Simm_medSS, Simm_medSoil)
PFF_summary <- rbind(PFF_medPM, PFF_medSO4, PFF_medNO3, PFF_medNH4, PFF_medBC, PFF_medOM, PFF_medSS, PFF_medSoil)
CARE_summary <- rbind(CARE_medPM, CARE_medSO4, CARE_medNO3, CARE_medNH4, CARE_medBC, CARE_medOM, CARE_medSS, CARE_medSoil)
CombinedCohorts_summary <- rbind(Simm_summary, PFF_summary, CARE_summary)
write_xlsx(CombinedCohorts_summary, "CombinedCohorts_TimeVaryingSummarizedExposures_2023_03_16.xlsx")
low_Simm <- Simm_medPM %>% filter(Median<8)
low_Simm <- left_join(low_Simm, Simm, by="ID")
low_Simm <- low_Simm %>% group_by(ID) %>% slice(1)
high_Simm <- Simm_medPM %>% filter(Median>=8)
high_Simm <- left_join(high_Simm, Simm, by="ID")
high_Simm <- high_Simm %>% group_by(ID) %>% slice(1)
low_PFF <- PFF_medPM %>% filter(Median<8)
low_PFF <- left_join(low_PFF, PFF, by="ID")
low_PFF <- low_PFF %>% group_by(ID) %>% slice(1)
high_PFF <- PFF_medPM %>% filter(Median>=8)
high_PFF <- left_join(high_PFF, PFF, by="ID")
high_PFF <- high_PFF %>% group_by(ID) %>% slice(1)
low_CARE <- CARE_medPM %>% filter(Median<8)
low_CARE <- left_join(low_CARE, CARE, by="ID")
low_CARE <- low_CARE %>% group_by(ID) %>% slice(1)
high_CARE <- CARE_medPM %>% filter(Median>=8)
high_CARE <- left_join(high_CARE, CARE, by="ID")
high_CARE <- high_CARE %>% group_by(ID) %>% slice(1)
n_prop_tbl <- function(x) {
tbl <- table(x)
res <- cbind(tbl, round(prop.table(tbl)*100,2))
colnames(res) <- c('Count', 'Percentage')
res
}
n_prop_tbl(low_Simm$sex)
## Count Percentage
## M 90 49.72
## F 91 50.28
n_prop_tbl(high_Simm$sex)
## Count Percentage
## M 705 56.72
## F 538 43.28
n_prop_tbl(low_PFF$sex.x)
## Warning: Unknown or uninitialised column: `sex.x`.
## Count Percentage
n_prop_tbl(high_PFF$sex.x)
## Warning: Unknown or uninitialised column: `sex.x`.
## Count Percentage
n_prop_tbl(low_CARE$sex.x)
## Warning: Unknown or uninitialised column: `sex.x`.
## Count Percentage
n_prop_tbl(high_CARE$sex.x)
## Warning: Unknown or uninitialised column: `sex.x`.
## Count Percentage
n_prop_tbl(low_Simm$dich_Race)
## Count Percentage
## White 168 92.82
## Non-White 13 7.18
n_prop_tbl(high_Simm$dich_Race)
## Count Percentage
## White 1090 87.69
## Non-White 153 12.31
n_prop_tbl(low_PFF$dich_Race.x)
## Warning: Unknown or uninitialised column: `dich_Race.x`.
## Count Percentage
n_prop_tbl(high_PFF$dich_Race.x)
## Warning: Unknown or uninitialised column: `dich_Race.x`.
## Count Percentage
n_prop_tbl(low_CARE$dich_Race.x)
## Warning: Unknown or uninitialised column: `dich_Race.x`.
## Count Percentage
n_prop_tbl(high_CARE$dich_Race.x)
## Warning: Unknown or uninitialised column: `dich_Race.x`.
## Count Percentage
n_prop_tbl(low_Simm$smokeHx)
## Count Percentage
## Never 69 38.12
## Former 61 33.70
## Always 5 2.76
## Unknown 46 25.41
n_prop_tbl(high_Simm$smokeHx)
## Count Percentage
## Never 344 27.67
## Former 603 48.51
## Always 33 2.65
## Unknown 263 21.16
n_prop_tbl(low_PFF$smokeHx.x)
## Warning: Unknown or uninitialised column: `smokeHx.x`.
## Count Percentage
n_prop_tbl(high_PFF$smokeHx.x)
## Warning: Unknown or uninitialised column: `smokeHx.x`.
## Count Percentage
n_prop_tbl(low_CARE$smokeHx.x)
## Warning: Unknown or uninitialised column: `smokeHx.x`.
## Count Percentage
n_prop_tbl(high_CARE$smokeHx.x)
## Warning: Unknown or uninitialised column: `smokeHx.x`.
## Count Percentage
n_prop_tbl(low_Simm$dx_group)
## Count Percentage
## IPF 67 37.02
## CTD-ILD 32 17.68
## HP 14 7.73
## OTHER_IIP 7 3.87
## OTHER_ILD 9 4.97
## PNEUMOCONIOSIS 3 1.66
## UNCLASSIFIABLE 49 27.07
n_prop_tbl(high_Simm$dx_group)
## Count Percentage
## IPF 649 52.21
## CTD-ILD 268 21.56
## HP 41 3.30
## OTHER_IIP 61 4.91
## OTHER_ILD 41 3.30
## PNEUMOCONIOSIS 23 1.85
## UNCLASSIFIABLE 160 12.87
n_prop_tbl(low_PFF$dx_group.x)
## Warning: Unknown or uninitialised column: `dx_group.x`.
## Count Percentage
n_prop_tbl(high_PFF$dx_group.x)
## Warning: Unknown or uninitialised column: `dx_group.x`.
## Count Percentage
n_prop_tbl(low_CARE$dx_group.x)
## Warning: Unknown or uninitialised column: `dx_group.x`.
## Count Percentage
n_prop_tbl(high_CARE$dx_group.x)
## Warning: Unknown or uninitialised column: `dx_group.x`.
## Count Percentage
summary(low_Simm$age_dx)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 17.81 59.59 67.30 64.95 72.57 89.23
summary(high_Simm$age_dx)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 20.22 57.63 65.51 64.10 72.58 94.06 1
summary(low_PFF$age_dx.x)
## Warning: Unknown or uninitialised column: `age_dx.x`.
## Length Class Mode
## 0 NULL NULL
summary(high_PFF$age_dx.x)
## Warning: Unknown or uninitialised column: `age_dx.x`.
## Length Class Mode
## 0 NULL NULL
summary(low_CARE$age_dx.x)
## Warning: Unknown or uninitialised column: `age_dx.x`.
## Length Class Mode
## 0 NULL NULL
summary(high_CARE$age_dx.x)
## Warning: Unknown or uninitialised column: `age_dx.x`.
## Length Class Mode
## 0 NULL NULL
summary(low_Simm$fvc_pct)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 26.00 58.50 72.00 72.42 86.00 138.00 38
summary(high_Simm$fvc_pct)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 16.00 53.00 65.00 66.32 79.20 124.17 309
summary(low_PFF$fvc_pct.x)
## Warning: Unknown or uninitialised column: `fvc_pct.x`.
## Length Class Mode
## 0 NULL NULL
summary(high_PFF$fvc_pct.x)
## Warning: Unknown or uninitialised column: `fvc_pct.x`.
## Length Class Mode
## 0 NULL NULL
summary(low_CARE$fvc_pct.x)
## Warning: Unknown or uninitialised column: `fvc_pct.x`.
## Length Class Mode
## 0 NULL NULL
summary(high_CARE$fvc_pct.x)
## Warning: Unknown or uninitialised column: `fvc_pct.x`.
## Length Class Mode
## 0 NULL NULL
summary(low_Simm$dlco_pct)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 9.00 38.00 52.50 51.91 63.00 101.00 45
summary(high_Simm$dlco_pct)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 14.00 36.70 49.00 50.94 63.24 124.00 377
summary(low_PFF$dlco_pct.x)
## Warning: Unknown or uninitialised column: `dlco_pct.x`.
## Length Class Mode
## 0 NULL NULL
summary(high_PFF$dlco_pct.x)
## Warning: Unknown or uninitialised column: `dlco_pct.x`.
## Length Class Mode
## 0 NULL NULL
summary(low_CARE$dlco_pct.x)
## Warning: Unknown or uninitialised column: `dlco_pct.x`.
## Length Class Mode
## 0 NULL NULL
summary(high_CARE$dlco_pct.x)
## Warning: Unknown or uninitialised column: `dlco_pct.x`.
## Length Class Mode
## 0 NULL NULL
summary(low_Simm$disadv)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.007971 0.378986 0.640580 0.581453 0.781884 1.000000 6
summary(high_Simm$disadv)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00145 0.24638 0.49928 0.49576 0.74275 1.00000 38
summary(low_PFF$disadv.x)
## Warning: Unknown or uninitialised column: `disadv.x`.
## Length Class Mode
## 0 NULL NULL
summary(high_PFF$disadv.x)
## Warning: Unknown or uninitialised column: `disadv.x`.
## Length Class Mode
## 0 NULL NULL
summary(low_CARE$disadv.x)
## Warning: Unknown or uninitialised column: `disadv.x`.
## Length Class Mode
## 0 NULL NULL
summary(high_CARE$disadv.x)
## Warning: Unknown or uninitialised column: `disadv.x`.
## Length Class Mode
## 0 NULL NULL
summary(low_Simm$time_DeathTxCensor)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.129 2.789 3.457 4.777 16.225 1
summary(high_Simm$time_DeathTxCensor)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.213 3.103 4.440 6.446 20.427 7
summary(low_PFF$time_DeathTxCensor.x)
## Warning: Unknown or uninitialised column: `time_DeathTxCensor.x`.
## Length Class Mode
## 0 NULL NULL
summary(high_PFF$time_DeathTxCensor.x)
## Warning: Unknown or uninitialised column: `time_DeathTxCensor.x`.
## Length Class Mode
## 0 NULL NULL
summary(low_CARE$time_DeathTxCensor.x)
## Warning: Unknown or uninitialised column: `time_DeathTxCensor.x`.
## Length Class Mode
## 0 NULL NULL
summary(high_CARE$time_DeathTxCensor.x)
## Warning: Unknown or uninitialised column: `time_DeathTxCensor.x`.
## Length Class Mode
## 0 NULL NULL
n_prop_tbl(low_Simm$status)
## Count Percentage
## 0 135 74.59
## 1 33 18.23
## 2 13 7.18
n_prop_tbl(high_Simm$status)
## Count Percentage
## 0 381 30.65
## 1 674 54.22
## 2 188 15.12
n_prop_tbl(low_PFF$status.x)
## Warning: Unknown or uninitialised column: `status.x`.
## Count Percentage
n_prop_tbl(high_PFF$status.x)
## Warning: Unknown or uninitialised column: `status.x`.
## Count Percentage
n_prop_tbl(low_CARE$status.x)
## Warning: Unknown or uninitialised column: `status.x`.
## Count Percentage
n_prop_tbl(high_CARE$status.x)
## Warning: Unknown or uninitialised column: `status.x`.
## Count Percentage
rm(Simm_medPM, Simm_medSO4, Simm_medNO3, Simm_medNH4, Simm_medBC, Simm_medOM, Simm_medSS, Simm_medSoil, PFF_medPM, PFF_medSO4, PFF_medNO3, PFF_medNH4, PFF_medBC, PFF_medOM, PFF_medSS, PFF_medSoil, CARE_medPM, CARE_medSO4, CARE_medNO3, CARE_medNH4, CARE_medBC, CARE_medOM, CARE_medSS, CARE_medSoil)
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ PM + dx_yr + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ PM + dx_yr + site,
## data = All, id = ID, cluster = cohort)
##
## n= 335367, number of events= 6459
## (1981 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## PM 0.084880 1.088586 0.005861 0.019561 4.339 1.43e-05 ***
## dx_yr 0.238088 1.268821 0.005528 0.137872 1.727 0.08419 .
## site02R 0.245409 1.278144 0.254458 0.097670 2.513 0.01198 *
## site03R -0.216998 0.804932 0.263211 0.080355 -2.700 0.00692 **
## site04R -0.490774 0.612152 0.271430 0.044364 -11.062 < 2e-16 ***
## site05R -0.535363 0.585457 0.276347 0.112053 -4.778 1.77e-06 ***
## site06R -0.132095 0.876258 0.267152 0.163661 -0.807 0.41959
## site07R -0.642670 0.525887 0.245835 0.123852 -5.189 2.11e-07 ***
## site09R -0.195108 0.822745 0.269373 0.033389 -5.844 5.11e-09 ***
## site1 -0.380259 0.683684 0.216379 0.001216 -312.734 < 2e-16 ***
## site101 -0.487996 0.613855 0.220322 0.198527 -2.458 0.01397 *
## site102 -0.420459 0.656745 0.220066 0.201687 -2.085 0.03710 *
## site103 -0.146977 0.863314 0.215499 0.149057 -0.986 0.32411
## site104 -0.326472 0.721464 0.221273 0.158771 -2.056 0.03976 *
## site105 -0.460720 0.630829 0.217575 0.181737 -2.535 0.01124 *
## site106 -0.572618 0.564047 0.219135 0.137488 -4.165 3.12e-05 ***
## site107 -0.127480 0.880311 0.240844 0.222476 -0.573 0.56664
## site108 -0.144464 0.865486 0.240161 0.382217 -0.378 0.70546
## site10R -0.362530 0.695913 0.289827 0.116403 -3.114 0.00184 **
## site11R -0.343532 0.709261 0.239099 0.038143 -9.006 < 2e-16 ***
## site12R -0.188462 0.828232 0.252337 0.031612 -5.962 2.49e-09 ***
## site13R -0.581341 0.559148 0.240868 0.116814 -4.977 6.47e-07 ***
## site14R -0.108380 0.897287 0.396068 0.221131 -0.490 0.62405
## site15R -0.169208 0.844333 0.270954 0.027394 -6.177 6.54e-10 ***
## site16R -0.041370 0.959474 0.263507 0.113692 -0.364 0.71595
## site17R -0.332971 0.716791 0.275862 0.063096 -5.277 1.31e-07 ***
## site18R -0.670048 0.511684 0.259886 0.208191 -3.218 0.00129 **
## site19R -0.041885 0.958980 0.282769 0.065682 -0.638 0.52367
## site20R -0.380513 0.683511 0.287442 0.044890 -8.476 < 2e-16 ***
## site21R -0.587648 0.555632 0.253164 0.064247 -9.147 < 2e-16 ***
## site22R -0.294333 0.745028 0.247772 0.037573 -7.834 4.74e-15 ***
## site23R -0.360936 0.697024 0.248582 0.034146 -10.570 < 2e-16 ***
## site24R -0.090378 0.913586 0.248862 0.115648 -0.781 0.43451
## site25R -0.388587 0.678015 0.251283 0.061813 -6.287 3.25e-10 ***
## site26R -0.565617 0.568009 0.265872 0.077287 -7.318 2.51e-13 ***
## site27R -0.352869 0.702669 0.369536 0.033790 -10.443 < 2e-16 ***
## site28R -0.395042 0.673651 0.266491 0.067926 -5.816 6.04e-09 ***
## site29R -0.490035 0.612605 0.335045 0.069483 -7.053 1.76e-12 ***
## site30R -0.639597 0.527505 0.256311 0.082909 -7.714 1.21e-14 ***
## site31R -0.268836 0.764269 0.270671 0.022380 -12.012 < 2e-16 ***
## site32R -0.612004 0.542263 0.268289 0.089629 -6.828 8.60e-12 ***
## site33R -0.456725 0.633354 0.257752 0.043123 -10.591 < 2e-16 ***
## site34R -0.307404 0.735354 0.247754 0.073720 -4.170 3.05e-05 ***
## site35R -0.112425 0.893664 0.264302 0.018541 -6.064 1.33e-09 ***
## site36R -0.482322 0.617348 0.254997 0.059858 -8.058 7.77e-16 ***
## site37R -0.519386 0.594886 0.257370 0.068392 -7.594 3.10e-14 ***
## site38R -0.447477 0.639239 0.261378 0.026047 -17.180 < 2e-16 ***
## site39R -0.358344 0.698833 0.289796 0.061832 -5.795 6.81e-09 ***
## site40R -0.541991 0.581589 0.268845 0.114117 -4.749 2.04e-06 ***
## site41R -0.466901 0.626942 0.252030 0.043890 -10.638 < 2e-16 ***
## site42R -0.135764 0.873049 0.275877 0.093730 -1.448 0.14749
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## PM 1.0886 0.9186 1.0476 1.1311
## dx_yr 1.2688 0.7881 0.9684 1.6625
## site02R 1.2781 0.7824 1.0555 1.5478
## site03R 0.8049 1.2423 0.6876 0.9422
## site04R 0.6122 1.6336 0.5612 0.6678
## site05R 0.5855 1.7081 0.4700 0.7292
## site06R 0.8763 1.1412 0.6358 1.2076
## site07R 0.5259 1.9016 0.4125 0.6704
## site09R 0.8227 1.2154 0.7706 0.8784
## site1 0.6837 1.4627 0.6821 0.6853
## site101 0.6139 1.6290 0.4160 0.9058
## site102 0.6567 1.5227 0.4423 0.9752
## site103 0.8633 1.1583 0.6446 1.1562
## site104 0.7215 1.3861 0.5285 0.9848
## site105 0.6308 1.5852 0.4418 0.9008
## site106 0.5640 1.7729 0.4308 0.7385
## site107 0.8803 1.1360 0.5692 1.3615
## site108 0.8655 1.1554 0.4092 1.8306
## site10R 0.6959 1.4370 0.5540 0.8743
## site11R 0.7093 1.4099 0.6582 0.7643
## site12R 0.8282 1.2074 0.7785 0.8812
## site13R 0.5591 1.7884 0.4447 0.7030
## site14R 0.8973 1.1145 0.5817 1.3841
## site15R 0.8443 1.1844 0.8002 0.8909
## site16R 0.9595 1.0422 0.7678 1.1990
## site17R 0.7168 1.3951 0.6334 0.8111
## site18R 0.5117 1.9543 0.3402 0.7695
## site19R 0.9590 1.0428 0.8431 1.0907
## site20R 0.6835 1.4630 0.6259 0.7464
## site21R 0.5556 1.7998 0.4899 0.6302
## site22R 0.7450 1.3422 0.6921 0.8020
## site23R 0.6970 1.4347 0.6519 0.7453
## site24R 0.9136 1.0946 0.7283 1.1460
## site25R 0.6780 1.4749 0.6007 0.7653
## site26R 0.5680 1.7605 0.4882 0.6609
## site27R 0.7027 1.4231 0.6576 0.7508
## site28R 0.6737 1.4844 0.5897 0.7696
## site29R 0.6126 1.6324 0.5346 0.7020
## site30R 0.5275 1.8957 0.4484 0.6206
## site31R 0.7643 1.3084 0.7315 0.7985
## site32R 0.5423 1.8441 0.4549 0.6464
## site33R 0.6334 1.5789 0.5820 0.6892
## site34R 0.7354 1.3599 0.6364 0.8497
## site35R 0.8937 1.1190 0.8618 0.9267
## site36R 0.6173 1.6198 0.5490 0.6942
## site37R 0.5949 1.6810 0.5203 0.6802
## site38R 0.6392 1.5644 0.6074 0.6727
## site39R 0.6988 1.4310 0.6191 0.7889
## site40R 0.5816 1.7194 0.4650 0.7274
## site41R 0.6269 1.5950 0.5753 0.6833
## site42R 0.8730 1.1454 0.7265 1.0491
##
## Concordance= 0.672 (se = 0.065 )
## Likelihood ratio test= 2514 on 51 df, p=<2e-16
## Wald test = 135.9 on 51 df, p=1e-09
## Score (logrank) test = 2141 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ PM + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ PM + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = All, id = ID,
## cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## PM 0.088556 1.092596 0.006029 0.020364 4.349 1.37e-05 ***
## dx_yr 0.235450 1.265478 0.005680 0.140489 1.676 0.093752 .
## age_dx 0.010062 1.010113 0.001176 0.003891 2.586 0.009706 **
## sexF -0.142647 0.867060 0.026705 0.059993 -2.378 0.017420 *
## dich_RaceNon-White -0.019531 0.980659 0.036895 0.024148 -0.809 0.418635
## smokeHxFormer 0.099200 1.104287 0.032889 0.048676 2.038 0.041553 *
## smokeHxAlways 0.031820 1.032332 0.074835 0.037264 0.854 0.393158
## smokeHxUnknown 0.032399 1.032930 0.070636 0.045101 0.718 0.472523
## smokeHxEver -0.010767 0.989290 0.049938 0.014588 -0.738 0.460464
## disadv -0.005585 0.994431 0.045354 0.056866 -0.098 0.921764
## site02R 0.211792 1.235891 0.258961 0.091144 2.324 0.020141 *
## site03R -0.207747 0.812413 0.267561 0.078655 -2.641 0.008260 **
## site04R -0.463205 0.629263 0.275735 0.020666 -22.414 < 2e-16 ***
## site05R -0.522535 0.593015 0.282492 0.134040 -3.898 9.69e-05 ***
## site06R -0.177896 0.837030 0.273135 0.200089 -0.889 0.373959
## site07R -0.696435 0.498359 0.250491 0.112083 -6.214 5.18e-10 ***
## site09R -0.252072 0.777189 0.275700 0.026438 -9.535 < 2e-16 ***
## site1 -0.451332 0.636779 0.223822 0.024934 -18.101 < 2e-16 ***
## site101 -0.509880 0.600568 0.227123 0.171241 -2.978 0.002906 **
## site102 -0.460996 0.630655 0.227214 0.182803 -2.522 0.011675 *
## site103 -0.174666 0.839737 0.222667 0.116934 -1.494 0.135249
## site104 -0.381590 0.682775 0.228202 0.120817 -3.158 0.001586 **
## site105 -0.520288 0.594350 0.224993 0.148989 -3.492 0.000479 ***
## site106 -0.686344 0.503413 0.226294 0.096139 -7.139 9.40e-13 ***
## site107 -0.189419 0.827440 0.247433 0.192632 -0.983 0.325451
## site108 -0.212134 0.808857 0.246457 0.348456 -0.609 0.542669
## site10R -0.422988 0.655087 0.296657 0.104715 -4.039 5.36e-05 ***
## site11R -0.423571 0.654705 0.245575 0.039387 -10.754 < 2e-16 ***
## site12R -0.239357 0.787134 0.256966 0.044150 -5.421 5.91e-08 ***
## site13R -0.674803 0.509257 0.245602 0.110496 -6.107 1.02e-09 ***
## site14R -0.105293 0.900061 0.398980 0.222689 -0.473 0.636339
## site15R -0.245542 0.782281 0.275615 0.043929 -5.590 2.28e-08 ***
## site16R -0.028750 0.971660 0.267664 0.113909 -0.252 0.800738
## site17R -0.306669 0.735894 0.280211 0.059062 -5.192 2.08e-07 ***
## site18R -0.722091 0.485736 0.264575 0.187169 -3.858 0.000114 ***
## site19R -0.093466 0.910769 0.287043 0.046770 -1.998 0.045671 *
## site20R -0.411981 0.662337 0.291694 0.046621 -8.837 < 2e-16 ***
## site21R -0.642845 0.525795 0.257732 0.082188 -7.822 5.21e-15 ***
## site22R -0.363549 0.695205 0.252567 0.048145 -7.551 4.31e-14 ***
## site23R -0.394406 0.674080 0.255427 0.052187 -7.558 4.11e-14 ***
## site24R -0.072320 0.930234 0.253705 0.117750 -0.614 0.539096
## site25R -0.437696 0.645522 0.257596 0.066434 -6.588 4.45e-11 ***
## site26R -0.626526 0.534445 0.270456 0.071865 -8.718 < 2e-16 ***
## site27R -0.347629 0.706361 0.372620 0.020876 -16.652 < 2e-16 ***
## site28R -0.332687 0.716994 0.271033 0.061239 -5.433 5.55e-08 ***
## site29R -0.540347 0.582546 0.339165 0.074672 -7.236 4.61e-13 ***
## site30R -0.708897 0.492187 0.261520 0.080032 -8.858 < 2e-16 ***
## site31R -0.272634 0.761371 0.274821 0.014096 -19.341 < 2e-16 ***
## site32R -0.620950 0.537434 0.278278 0.065385 -9.497 < 2e-16 ***
## site33R -0.502324 0.605123 0.263267 0.035671 -14.082 < 2e-16 ***
## site34R -0.360238 0.697510 0.252924 0.066956 -5.380 7.44e-08 ***
## site35R -0.175949 0.838661 0.270129 0.015505 -11.348 < 2e-16 ***
## site36R -0.496404 0.608716 0.259614 0.067086 -7.400 1.37e-13 ***
## site37R -0.578580 0.560694 0.261900 0.065259 -8.866 < 2e-16 ***
## site38R -0.480567 0.618433 0.266917 0.022155 -21.691 < 2e-16 ***
## site39R -0.424512 0.654089 0.294033 0.043745 -9.704 < 2e-16 ***
## site40R -0.620467 0.537693 0.274443 0.110308 -5.625 1.86e-08 ***
## site41R -0.487068 0.614425 0.256422 0.037283 -13.064 < 2e-16 ***
## site42R -0.149197 0.861399 0.279960 0.096399 -1.548 0.121695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## PM 1.0926 0.9153 1.0498 1.1371
## dx_yr 1.2655 0.7902 0.9609 1.6666
## age_dx 1.0101 0.9900 1.0024 1.0178
## sexF 0.8671 1.1533 0.7709 0.9752
## dich_RaceNon-White 0.9807 1.0197 0.9353 1.0282
## smokeHxFormer 1.1043 0.9056 1.0038 1.2148
## smokeHxAlways 1.0323 0.9687 0.9596 1.1106
## smokeHxUnknown 1.0329 0.9681 0.9455 1.1284
## smokeHxEver 0.9893 1.0108 0.9614 1.0180
## disadv 0.9944 1.0056 0.8895 1.1117
## site02R 1.2359 0.8091 1.0337 1.4776
## site03R 0.8124 1.2309 0.6963 0.9478
## site04R 0.6293 1.5892 0.6043 0.6553
## site05R 0.5930 1.6863 0.4560 0.7712
## site06R 0.8370 1.1947 0.5655 1.2390
## site07R 0.4984 2.0066 0.4001 0.6208
## site09R 0.7772 1.2867 0.7379 0.8185
## site1 0.6368 1.5704 0.6064 0.6687
## site101 0.6006 1.6651 0.4293 0.8401
## site102 0.6307 1.5857 0.4407 0.9024
## site103 0.8397 1.1908 0.6677 1.0560
## site104 0.6828 1.4646 0.5388 0.8652
## site105 0.5943 1.6825 0.4438 0.7959
## site106 0.5034 1.9864 0.4170 0.6078
## site107 0.8274 1.2085 0.5672 1.2070
## site108 0.8089 1.2363 0.4086 1.6013
## site10R 0.6551 1.5265 0.5335 0.8043
## site11R 0.6547 1.5274 0.6061 0.7072
## site12R 0.7871 1.2704 0.7219 0.8583
## site13R 0.5093 1.9636 0.4101 0.6324
## site14R 0.9001 1.1110 0.5817 1.3926
## site15R 0.7823 1.2783 0.7177 0.8526
## site16R 0.9717 1.0292 0.7772 1.2147
## site17R 0.7359 1.3589 0.6555 0.8262
## site18R 0.4857 2.0587 0.3366 0.7010
## site19R 0.9108 1.0980 0.8310 0.9982
## site20R 0.6623 1.5098 0.6045 0.7257
## site21R 0.5258 1.9019 0.4476 0.6177
## site22R 0.6952 1.4384 0.6326 0.7640
## site23R 0.6741 1.4835 0.6085 0.7467
## site24R 0.9302 1.0750 0.7385 1.1717
## site25R 0.6455 1.5491 0.5667 0.7353
## site26R 0.5344 1.8711 0.4642 0.6153
## site27R 0.7064 1.4157 0.6780 0.7359
## site28R 0.7170 1.3947 0.6359 0.8084
## site29R 0.5825 1.7166 0.5032 0.6744
## site30R 0.4922 2.0317 0.4207 0.5758
## site31R 0.7614 1.3134 0.7406 0.7827
## site32R 0.5374 1.8607 0.4728 0.6109
## site33R 0.6051 1.6526 0.5643 0.6489
## site34R 0.6975 1.4337 0.6117 0.7953
## site35R 0.8387 1.1924 0.8136 0.8645
## site36R 0.6087 1.6428 0.5337 0.6943
## site37R 0.5607 1.7835 0.4934 0.6372
## site38R 0.6184 1.6170 0.5922 0.6459
## site39R 0.6541 1.5288 0.6003 0.7126
## site40R 0.5377 1.8598 0.4332 0.6675
## site41R 0.6144 1.6275 0.5711 0.6610
## site42R 0.8614 1.1609 0.7131 1.0405
##
## Concordance= 0.676 (se = 0.062 )
## Likelihood ratio test= 2687 on 59 df, p=<2e-16
## Wald test = 122.2 on 59 df, p=3e-06
## Score (logrank) test = 2291 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
summary(All$PM)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.275 6.121 8.003 8.101 9.580 43.788 688
IQR(All$PM, na.rm=T)
## [1] 3.459232
# Will use the 5yr pre-censoring IQR (2.592959), not this one
All <- All %>% mutate(PM_IQR = PM/2.592959)
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ PM_IQR + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ PM_IQR + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All,
## id = ID, cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## PM_IQR 0.229623 1.258125 0.015633 0.052803 4.349 1.37e-05 ***
## dx_yr 0.235450 1.265478 0.005680 0.140489 1.676 0.093752 .
## age_dx 0.010062 1.010113 0.001176 0.003891 2.586 0.009706 **
## sexF -0.142647 0.867060 0.026705 0.059993 -2.378 0.017420 *
## dich_RaceNon-White -0.019531 0.980659 0.036895 0.024148 -0.809 0.418635
## smokeHxFormer 0.099200 1.104287 0.032889 0.048676 2.038 0.041553 *
## smokeHxAlways 0.031820 1.032332 0.074835 0.037264 0.854 0.393158
## smokeHxUnknown 0.032399 1.032930 0.070636 0.045101 0.718 0.472523
## smokeHxEver -0.010767 0.989290 0.049938 0.014588 -0.738 0.460464
## disadv -0.005585 0.994431 0.045354 0.056866 -0.098 0.921764
## site02R 0.211792 1.235891 0.258961 0.091144 2.324 0.020141 *
## site03R -0.207747 0.812413 0.267561 0.078655 -2.641 0.008260 **
## site04R -0.463205 0.629263 0.275735 0.020666 -22.414 < 2e-16 ***
## site05R -0.522535 0.593015 0.282492 0.134040 -3.898 9.69e-05 ***
## site06R -0.177896 0.837030 0.273135 0.200089 -0.889 0.373959
## site07R -0.696435 0.498359 0.250491 0.112083 -6.214 5.18e-10 ***
## site09R -0.252072 0.777189 0.275700 0.026438 -9.535 < 2e-16 ***
## site1 -0.451332 0.636779 0.223822 0.024934 -18.101 < 2e-16 ***
## site101 -0.509880 0.600568 0.227123 0.171241 -2.978 0.002906 **
## site102 -0.460996 0.630655 0.227214 0.182803 -2.522 0.011675 *
## site103 -0.174666 0.839737 0.222667 0.116934 -1.494 0.135249
## site104 -0.381590 0.682775 0.228202 0.120817 -3.158 0.001586 **
## site105 -0.520288 0.594350 0.224993 0.148989 -3.492 0.000479 ***
## site106 -0.686344 0.503413 0.226294 0.096139 -7.139 9.40e-13 ***
## site107 -0.189419 0.827440 0.247433 0.192632 -0.983 0.325451
## site108 -0.212134 0.808857 0.246457 0.348456 -0.609 0.542669
## site10R -0.422988 0.655087 0.296657 0.104715 -4.039 5.36e-05 ***
## site11R -0.423571 0.654705 0.245575 0.039387 -10.754 < 2e-16 ***
## site12R -0.239357 0.787134 0.256966 0.044150 -5.421 5.91e-08 ***
## site13R -0.674803 0.509257 0.245602 0.110496 -6.107 1.02e-09 ***
## site14R -0.105293 0.900061 0.398980 0.222689 -0.473 0.636339
## site15R -0.245542 0.782281 0.275615 0.043929 -5.590 2.28e-08 ***
## site16R -0.028750 0.971660 0.267664 0.113909 -0.252 0.800738
## site17R -0.306669 0.735894 0.280211 0.059062 -5.192 2.08e-07 ***
## site18R -0.722091 0.485736 0.264575 0.187169 -3.858 0.000114 ***
## site19R -0.093466 0.910769 0.287043 0.046770 -1.998 0.045671 *
## site20R -0.411981 0.662337 0.291694 0.046621 -8.837 < 2e-16 ***
## site21R -0.642845 0.525795 0.257732 0.082188 -7.822 5.21e-15 ***
## site22R -0.363549 0.695205 0.252567 0.048145 -7.551 4.31e-14 ***
## site23R -0.394406 0.674080 0.255427 0.052187 -7.558 4.11e-14 ***
## site24R -0.072320 0.930234 0.253705 0.117750 -0.614 0.539096
## site25R -0.437696 0.645522 0.257596 0.066434 -6.588 4.45e-11 ***
## site26R -0.626526 0.534445 0.270456 0.071865 -8.718 < 2e-16 ***
## site27R -0.347629 0.706361 0.372620 0.020876 -16.652 < 2e-16 ***
## site28R -0.332687 0.716994 0.271033 0.061239 -5.433 5.55e-08 ***
## site29R -0.540347 0.582546 0.339165 0.074672 -7.236 4.61e-13 ***
## site30R -0.708897 0.492187 0.261520 0.080032 -8.858 < 2e-16 ***
## site31R -0.272634 0.761371 0.274821 0.014096 -19.341 < 2e-16 ***
## site32R -0.620950 0.537434 0.278278 0.065385 -9.497 < 2e-16 ***
## site33R -0.502324 0.605123 0.263267 0.035671 -14.082 < 2e-16 ***
## site34R -0.360238 0.697510 0.252924 0.066956 -5.380 7.44e-08 ***
## site35R -0.175949 0.838661 0.270129 0.015505 -11.348 < 2e-16 ***
## site36R -0.496404 0.608716 0.259614 0.067086 -7.400 1.37e-13 ***
## site37R -0.578580 0.560694 0.261900 0.065259 -8.866 < 2e-16 ***
## site38R -0.480567 0.618433 0.266917 0.022155 -21.691 < 2e-16 ***
## site39R -0.424512 0.654089 0.294033 0.043745 -9.704 < 2e-16 ***
## site40R -0.620467 0.537693 0.274443 0.110308 -5.625 1.86e-08 ***
## site41R -0.487068 0.614425 0.256422 0.037283 -13.064 < 2e-16 ***
## site42R -0.149197 0.861399 0.279960 0.096399 -1.548 0.121695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## PM_IQR 1.2581 0.7948 1.1344 1.3953
## dx_yr 1.2655 0.7902 0.9609 1.6666
## age_dx 1.0101 0.9900 1.0024 1.0178
## sexF 0.8671 1.1533 0.7709 0.9752
## dich_RaceNon-White 0.9807 1.0197 0.9353 1.0282
## smokeHxFormer 1.1043 0.9056 1.0038 1.2148
## smokeHxAlways 1.0323 0.9687 0.9596 1.1106
## smokeHxUnknown 1.0329 0.9681 0.9455 1.1284
## smokeHxEver 0.9893 1.0108 0.9614 1.0180
## disadv 0.9944 1.0056 0.8895 1.1117
## site02R 1.2359 0.8091 1.0337 1.4776
## site03R 0.8124 1.2309 0.6963 0.9478
## site04R 0.6293 1.5892 0.6043 0.6553
## site05R 0.5930 1.6863 0.4560 0.7712
## site06R 0.8370 1.1947 0.5655 1.2390
## site07R 0.4984 2.0066 0.4001 0.6208
## site09R 0.7772 1.2867 0.7379 0.8185
## site1 0.6368 1.5704 0.6064 0.6687
## site101 0.6006 1.6651 0.4293 0.8401
## site102 0.6307 1.5857 0.4407 0.9024
## site103 0.8397 1.1908 0.6677 1.0560
## site104 0.6828 1.4646 0.5388 0.8652
## site105 0.5943 1.6825 0.4438 0.7959
## site106 0.5034 1.9864 0.4170 0.6078
## site107 0.8274 1.2085 0.5672 1.2070
## site108 0.8089 1.2363 0.4086 1.6013
## site10R 0.6551 1.5265 0.5335 0.8043
## site11R 0.6547 1.5274 0.6061 0.7072
## site12R 0.7871 1.2704 0.7219 0.8583
## site13R 0.5093 1.9636 0.4101 0.6324
## site14R 0.9001 1.1110 0.5817 1.3926
## site15R 0.7823 1.2783 0.7177 0.8526
## site16R 0.9717 1.0292 0.7772 1.2147
## site17R 0.7359 1.3589 0.6555 0.8262
## site18R 0.4857 2.0587 0.3366 0.7010
## site19R 0.9108 1.0980 0.8310 0.9982
## site20R 0.6623 1.5098 0.6045 0.7257
## site21R 0.5258 1.9019 0.4476 0.6177
## site22R 0.6952 1.4384 0.6326 0.7640
## site23R 0.6741 1.4835 0.6085 0.7467
## site24R 0.9302 1.0750 0.7385 1.1717
## site25R 0.6455 1.5491 0.5667 0.7353
## site26R 0.5344 1.8711 0.4642 0.6153
## site27R 0.7064 1.4157 0.6780 0.7359
## site28R 0.7170 1.3947 0.6359 0.8084
## site29R 0.5825 1.7166 0.5032 0.6744
## site30R 0.4922 2.0317 0.4207 0.5758
## site31R 0.7614 1.3134 0.7406 0.7827
## site32R 0.5374 1.8607 0.4728 0.6109
## site33R 0.6051 1.6526 0.5643 0.6489
## site34R 0.6975 1.4337 0.6117 0.7953
## site35R 0.8387 1.1924 0.8136 0.8645
## site36R 0.6087 1.6428 0.5337 0.6943
## site37R 0.5607 1.7835 0.4934 0.6372
## site38R 0.6184 1.6170 0.5922 0.6459
## site39R 0.6541 1.5288 0.6003 0.7126
## site40R 0.5377 1.8598 0.4332 0.6675
## site41R 0.6144 1.6275 0.5711 0.6610
## site42R 0.8614 1.1609 0.7131 1.0405
##
## Concordance= 0.676 (se = 0.062 )
## Likelihood ratio test= 2687 on 59 df, p=<2e-16
## Wald test = 122.2 on 59 df, p=3e-06
## Score (logrank) test = 2291 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
So this indicates that there is a HR of 1.26 per IQR increase in PM2.5 as compared with a HR of 1.09 per 1ug/m3 increase in PM2.5.
Base model
#First need to make dataframe that only includes patients with a value for event
Allx <- All %>% filter(!is.na(PM) & !is.na(deadORtx) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(cohort) & !is.na(site) & PM<20)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$PM, df=3) + Allx$dx_yr + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$PM, exp(predicted$fit), type="n", xlim=c(0,20), ylim=c(0,15))
lines(sm.spline(Allx$PM, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$PM, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$PM, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
Complete model
#First need to make dataframe that only includes patients with time_DeathTxCensor
Allx <- All %>% filter(!is.na(PM) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(deadORtx) & !is.na(age_dx) & !is.na(sex) & !is.na(smokeHx) & !is.na(dich_Race) & !is.na(disadv) & !is.na(site) & PM<20)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$PM, df=3) + Allx$dx_yr + Allx$age_dx + Allx$sex + Allx$smokeHx + Allx$dich_Race + Allx$disadv + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$PM, exp(predicted$fit), type="n", xlim=c(0,20), ylim=c(0,10))
lines(sm.spline(Allx$PM, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$PM, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$PM, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ SO4 + dx_yr + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SO4 + dx_yr +
## site, data = All, id = ID, cluster = cohort)
##
## n= 335367, number of events= 6459
## (1981 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## SO4 0.748461 2.113744 0.022746 0.089857 8.329 < 2e-16 ***
## dx_yr 0.347537 1.415577 0.006923 0.123523 2.814 0.004900 **
## site02R 0.152264 1.164468 0.254111 0.108620 1.402 0.160974
## site03R -0.439030 0.644662 0.263350 0.121680 -3.608 0.000308 ***
## site04R -1.098973 0.333213 0.271994 0.133146 -8.254 < 2e-16 ***
## site05R -1.483008 0.226954 0.278023 0.096035 -15.442 < 2e-16 ***
## site06R -0.928201 0.395264 0.268446 0.073787 -12.579 < 2e-16 ***
## site07R -1.348089 0.259736 0.246965 0.208336 -6.471 9.75e-11 ***
## site09R -0.700805 0.496186 0.269894 0.082708 -8.473 < 2e-16 ***
## site1 -1.516675 0.219440 0.220067 0.100879 -15.035 < 2e-16 ***
## site101 -1.112890 0.328608 0.221349 0.228563 -4.869 1.12e-06 ***
## site102 -0.599315 0.549188 0.220135 0.158515 -3.781 0.000156 ***
## site103 -0.295001 0.744531 0.215592 0.132142 -2.232 0.025585 *
## site104 -0.595559 0.551255 0.222107 0.162758 -3.659 0.000253 ***
## site105 -0.964830 0.381048 0.218249 0.214658 -4.495 6.97e-06 ***
## site106 -1.258365 0.284118 0.220345 0.205157 -6.134 8.59e-10 ***
## site107 -0.666938 0.513278 0.241526 0.229282 -2.909 0.003628 **
## site108 -0.580101 0.559842 0.240487 0.302802 -1.916 0.055394 .
## site10R -0.841165 0.431208 0.290331 0.167055 -5.035 4.77e-07 ***
## site11R -1.100279 0.332778 0.240375 0.134139 -8.203 2.35e-16 ***
## site12R -0.588863 0.554958 0.252693 0.049133 -11.985 < 2e-16 ***
## site13R -1.361731 0.256217 0.242270 0.191679 -7.104 1.21e-12 ***
## site14R -0.747290 0.473648 0.396600 0.112661 -6.633 3.29e-11 ***
## site15R -0.838237 0.432472 0.271845 0.098381 -8.520 < 2e-16 ***
## site16R -0.565078 0.568316 0.264023 0.050194 -11.258 < 2e-16 ***
## site17R -1.071869 0.342368 0.276904 0.100849 -10.628 < 2e-16 ***
## site18R -1.423026 0.240984 0.261137 0.277700 -5.124 2.99e-07 ***
## site19R -0.254052 0.775651 0.282815 0.077106 -3.295 0.000985 ***
## site20R -0.980594 0.375088 0.288115 0.083073 -11.804 < 2e-16 ***
## site21R -1.314766 0.268537 0.254172 0.107753 -12.202 < 2e-16 ***
## site22R -0.945716 0.388401 0.248622 0.088630 -10.670 < 2e-16 ***
## site23R -1.067863 0.343742 0.249665 0.104785 -10.191 < 2e-16 ***
## site24R -0.445988 0.640191 0.249109 0.039215 -11.373 < 2e-16 ***
## site25R -0.420806 0.656517 0.250645 0.110280 -3.816 0.000136 ***
## site26R -1.438868 0.237196 0.267596 0.209953 -6.853 7.22e-12 ***
## site27R -1.140559 0.319640 0.370458 0.126148 -9.041 < 2e-16 ***
## site28R -1.351952 0.258735 0.268286 0.122315 -11.053 < 2e-16 ***
## site29R -1.101639 0.332326 0.335670 0.146025 -7.544 4.55e-14 ***
## site30R -1.250839 0.286265 0.256798 0.201281 -6.214 5.15e-10 ***
## site31R -1.036938 0.354539 0.271796 0.083542 -12.412 < 2e-16 ***
## site32R -0.741550 0.476375 0.268181 0.139929 -5.299 1.16e-07 ***
## site33R -1.290355 0.275173 0.259306 0.166805 -7.736 1.03e-14 ***
## site34R -0.982762 0.374276 0.248743 0.140681 -6.986 2.83e-12 ***
## site35R -0.551403 0.576141 0.264656 0.059851 -9.213 < 2e-16 ***
## site36R -1.397454 0.247226 0.256778 0.113584 -12.303 < 2e-16 ***
## site37R -1.337976 0.262376 0.258777 0.170555 -7.845 4.34e-15 ***
## site38R -1.150914 0.316347 0.262414 0.097680 -11.783 < 2e-16 ***
## site39R -0.438723 0.644859 0.289769 0.090481 -4.849 1.24e-06 ***
## site40R -0.681071 0.506075 0.266599 0.204503 -3.330 0.000867 ***
## site41R -1.011483 0.363679 0.252629 0.110912 -9.120 < 2e-16 ***
## site42R -0.915697 0.400237 0.277037 0.067731 -13.520 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SO4 2.1137 0.4731 1.7724 2.5208
## dx_yr 1.4156 0.7064 1.1112 1.8033
## site02R 1.1645 0.8588 0.9412 1.4407
## site03R 0.6447 1.5512 0.5079 0.8183
## site04R 0.3332 3.0011 0.2567 0.4326
## site05R 0.2270 4.4062 0.1880 0.2740
## site06R 0.3953 2.5300 0.3420 0.4568
## site07R 0.2597 3.8501 0.1727 0.3907
## site09R 0.4962 2.0154 0.4219 0.5835
## site1 0.2194 4.5570 0.1801 0.2674
## site101 0.3286 3.0431 0.2100 0.5143
## site102 0.5492 1.8209 0.4025 0.7493
## site103 0.7445 1.3431 0.5746 0.9646
## site104 0.5513 1.8140 0.4007 0.7584
## site105 0.3810 2.6243 0.2502 0.5804
## site106 0.2841 3.5197 0.1901 0.4247
## site107 0.5133 1.9483 0.3275 0.8045
## site108 0.5598 1.7862 0.3093 1.0135
## site10R 0.4312 2.3191 0.3108 0.5983
## site11R 0.3328 3.0050 0.2558 0.4328
## site12R 0.5550 1.8019 0.5040 0.6111
## site13R 0.2562 3.9029 0.1760 0.3730
## site14R 0.4736 2.1113 0.3798 0.5907
## site15R 0.4325 2.3123 0.3566 0.5244
## site16R 0.5683 1.7596 0.5151 0.6271
## site17R 0.3424 2.9208 0.2810 0.4172
## site18R 0.2410 4.1497 0.1398 0.4153
## site19R 0.7757 1.2892 0.6669 0.9022
## site20R 0.3751 2.6660 0.3187 0.4414
## site21R 0.2685 3.7239 0.2174 0.3317
## site22R 0.3884 2.5747 0.3265 0.4621
## site23R 0.3437 2.9092 0.2799 0.4221
## site24R 0.6402 1.5620 0.5928 0.6913
## site25R 0.6565 1.5232 0.5289 0.8149
## site26R 0.2372 4.2159 0.1572 0.3579
## site27R 0.3196 3.1285 0.2496 0.4093
## site28R 0.2587 3.8650 0.2036 0.3288
## site29R 0.3323 3.0091 0.2496 0.4424
## site30R 0.2863 3.4933 0.1929 0.4247
## site31R 0.3545 2.8206 0.3010 0.4176
## site32R 0.4764 2.0992 0.3621 0.6267
## site33R 0.2752 3.6341 0.1984 0.3816
## site34R 0.3743 2.6718 0.2841 0.4931
## site35R 0.5761 1.7357 0.5124 0.6478
## site36R 0.2472 4.0449 0.1979 0.3089
## site37R 0.2624 3.8113 0.1878 0.3665
## site38R 0.3163 3.1611 0.2612 0.3831
## site39R 0.6449 1.5507 0.5401 0.7700
## site40R 0.5061 1.9760 0.3390 0.7556
## site41R 0.3637 2.7497 0.2926 0.4520
## site42R 0.4002 2.4985 0.3505 0.4571
##
## Concordance= 0.68 (se = 0.066 )
## Likelihood ratio test= 3031 on 51 df, p=<2e-16
## Wald test = 220.3 on 51 df, p=<2e-16
## Score (logrank) test = 2466 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ SO4 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SO4 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All,
## id = ID, cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## SO4 0.7486195 2.1140794 0.0228978 0.0801980 9.335 < 2e-16
## dx_yr 0.3436630 1.4101033 0.0070602 0.1252699 2.743 0.006081
## age_dx 0.0091231 1.0091649 0.0011770 0.0034138 2.672 0.007531
## sexF -0.1358048 0.8730131 0.0267003 0.0629412 -2.158 0.030955
## dich_RaceNon-White 0.0173323 1.0174833 0.0368078 0.0356521 0.486 0.626861
## smokeHxFormer 0.1214572 1.1291410 0.0328996 0.0232463 5.225 1.74e-07
## smokeHxAlways 0.0250676 1.0253845 0.0748671 0.0601250 0.417 0.676733
## smokeHxUnknown -0.0534333 0.9479691 0.0705105 0.0842372 -0.634 0.525872
## smokeHxEver -0.0001398 0.9998603 0.0498816 0.0146130 -0.010 0.992369
## disadv 0.0194098 1.0195994 0.0456339 0.0706709 0.275 0.783585
## site02R 0.1394609 1.1496539 0.2585339 0.1014674 1.374 0.169305
## site03R -0.4106006 0.6632518 0.2676881 0.1151425 -3.566 0.000362
## site04R -1.0460758 0.3513137 0.2762447 0.1211479 -8.635 < 2e-16
## site05R -1.4455201 0.2356235 0.2840259 0.0568837 -25.412 < 2e-16
## site06R -0.9516495 0.3861036 0.2742355 0.0962182 -9.891 < 2e-16
## site07R -1.3756514 0.2526750 0.2514679 0.1862686 -7.385 1.52e-13
## site09R -0.7416434 0.4763305 0.2761663 0.0673538 -11.011 < 2e-16
## site1 -1.5544149 0.2113130 0.2274027 0.0719897 -21.592 < 2e-16
## site101 -1.1335593 0.3218855 0.2282521 0.2010436 -5.638 1.72e-08
## site102 -0.6376679 0.5285236 0.2272866 0.1546209 -4.124 3.72e-05
## site103 -0.3352224 0.7151790 0.2227826 0.1082563 -3.097 0.001958
## site104 -0.6595980 0.5170592 0.2289029 0.1281763 -5.146 2.66e-07
## site105 -1.0161273 0.3619941 0.2257368 0.1881986 -5.399 6.69e-08
## site106 -1.3566176 0.2575304 0.2275374 0.1704373 -7.960 1.73e-15
## site107 -0.7176227 0.4879108 0.2481484 0.2087767 -3.437 0.000588
## site108 -0.6447068 0.5248164 0.2468598 0.2847586 -2.264 0.023571
## site10R -0.8852539 0.4126094 0.2970140 0.1527895 -5.794 6.88e-09
## site11R -1.1588617 0.3138432 0.2466684 0.1069997 -10.831 < 2e-16
## site12R -0.6236159 0.5360028 0.2572520 0.0365245 -17.074 < 2e-16
## site13R -1.4270846 0.2400076 0.2468433 0.1774411 -8.043 8.80e-16
## site14R -0.7228767 0.4853540 0.3994901 0.1160683 -6.228 4.72e-10
## site15R -0.8985221 0.4071710 0.2764243 0.0811261 -11.076 < 2e-16
## site16R -0.5418222 0.5816873 0.2681892 0.0413544 -13.102 < 2e-16
## site17R -1.0283076 0.3576117 0.2811913 0.0646268 -15.911 < 2e-16
## site18R -1.4565403 0.2330411 0.2657102 0.2465883 -5.907 3.49e-09
## site19R -0.2920568 0.7467261 0.2870797 0.0632030 -4.621 3.82e-06
## site20R -0.9920734 0.3708070 0.2922973 0.0635554 -15.610 < 2e-16
## site21R -1.3475211 0.2598837 0.2586169 0.0881674 -15.284 < 2e-16
## site22R -0.9842263 0.3737283 0.2532427 0.0724626 -13.583 < 2e-16
## site23R -1.0841697 0.3381825 0.2564349 0.0770784 -14.066 < 2e-16
## site24R -0.4111608 0.6628803 0.2539202 0.0319734 -12.859 < 2e-16
## site25R -0.4514899 0.6366789 0.2568174 0.1107135 -4.078 4.54e-05
## site26R -1.4838953 0.2267527 0.2720764 0.1871254 -7.930 2.19e-15
## site27R -1.1160514 0.3275707 0.3734863 0.1067333 -10.456 < 2e-16
## site28R -1.2792304 0.2782514 0.2729314 0.0779668 -16.407 < 2e-16
## site29R -1.1244751 0.3248229 0.3397708 0.1540943 -7.297 2.94e-13
## site30R -1.2954233 0.2737820 0.2618878 0.1858099 -6.972 3.13e-12
## site31R -1.0238891 0.3591953 0.2758866 0.0619911 -16.517 < 2e-16
## site32R -0.7423082 0.4760139 0.2781651 0.1166662 -6.363 1.98e-10
## site33R -1.3058681 0.2709372 0.2646573 0.1301099 -10.037 < 2e-16
## site34R -1.0127968 0.3632018 0.2538071 0.1276968 -7.931 2.17e-15
## site35R -0.5995747 0.5490451 0.2704420 0.0450046 -13.323 < 2e-16
## site36R -1.3918254 0.2486211 0.2612833 0.0821215 -16.948 < 2e-16
## site37R -1.3688891 0.2543894 0.2631513 0.1521625 -8.996 < 2e-16
## site38R -1.1675748 0.3111206 0.2678772 0.0735074 -15.884 < 2e-16
## site39R -0.4805553 0.6184399 0.2939729 0.0747571 -6.428 1.29e-10
## site40R -0.7233672 0.4851160 0.2719657 0.1854047 -3.902 9.56e-05
## site41R -1.0144742 0.3625930 0.2569589 0.0935923 -10.839 < 2e-16
## site42R -0.9088722 0.4029784 0.2810688 0.0448728 -20.254 < 2e-16
##
## SO4 ***
## dx_yr **
## age_dx **
## sexF *
## dich_RaceNon-White
## smokeHxFormer ***
## smokeHxAlways
## smokeHxUnknown
## smokeHxEver
## disadv
## site02R
## site03R ***
## site04R ***
## site05R ***
## site06R ***
## site07R ***
## site09R ***
## site1 ***
## site101 ***
## site102 ***
## site103 **
## site104 ***
## site105 ***
## site106 ***
## site107 ***
## site108 *
## site10R ***
## site11R ***
## site12R ***
## site13R ***
## site14R ***
## site15R ***
## site16R ***
## site17R ***
## site18R ***
## site19R ***
## site20R ***
## site21R ***
## site22R ***
## site23R ***
## site24R ***
## site25R ***
## site26R ***
## site27R ***
## site28R ***
## site29R ***
## site30R ***
## site31R ***
## site32R ***
## site33R ***
## site34R ***
## site35R ***
## site36R ***
## site37R ***
## site38R ***
## site39R ***
## site40R ***
## site41R ***
## site42R ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SO4 2.1141 0.4730 1.8066 2.4739
## dx_yr 1.4101 0.7092 1.1031 1.8025
## age_dx 1.0092 0.9909 1.0024 1.0159
## sexF 0.8730 1.1455 0.7717 0.9876
## dich_RaceNon-White 1.0175 0.9828 0.9488 1.0911
## smokeHxFormer 1.1291 0.8856 1.0788 1.1818
## smokeHxAlways 1.0254 0.9752 0.9114 1.1536
## smokeHxUnknown 0.9480 1.0549 0.8037 1.1181
## smokeHxEver 0.9999 1.0001 0.9716 1.0289
## disadv 1.0196 0.9808 0.8877 1.1711
## site02R 1.1497 0.8698 0.9423 1.4026
## site03R 0.6633 1.5077 0.5293 0.8312
## site04R 0.3513 2.8465 0.2771 0.4455
## site05R 0.2356 4.2441 0.2108 0.2634
## site06R 0.3861 2.5900 0.3197 0.4662
## site07R 0.2527 3.9577 0.1754 0.3640
## site09R 0.4763 2.0994 0.4174 0.5436
## site1 0.2113 4.7323 0.1835 0.2433
## site101 0.3219 3.1067 0.2171 0.4773
## site102 0.5285 1.8921 0.3903 0.7156
## site103 0.7152 1.3983 0.5785 0.8842
## site104 0.5171 1.9340 0.4022 0.6647
## site105 0.3620 2.7625 0.2503 0.5235
## site106 0.2575 3.8830 0.1844 0.3597
## site107 0.4879 2.0496 0.3241 0.7346
## site108 0.5248 1.9054 0.3003 0.9171
## site10R 0.4126 2.4236 0.3058 0.5567
## site11R 0.3138 3.1863 0.2545 0.3871
## site12R 0.5360 1.8657 0.4990 0.5758
## site13R 0.2400 4.1665 0.1695 0.3398
## site14R 0.4854 2.0604 0.3866 0.6093
## site15R 0.4072 2.4560 0.3473 0.4773
## site16R 0.5817 1.7191 0.5364 0.6308
## site17R 0.3576 2.7963 0.3151 0.4059
## site18R 0.2330 4.2911 0.1437 0.3779
## site19R 0.7467 1.3392 0.6597 0.8452
## site20R 0.3708 2.6968 0.3274 0.4200
## site21R 0.2599 3.8479 0.2186 0.3089
## site22R 0.3737 2.6757 0.3242 0.4308
## site23R 0.3382 2.9570 0.2908 0.3933
## site24R 0.6629 1.5086 0.6226 0.7058
## site25R 0.6367 1.5707 0.5125 0.7910
## site26R 0.2268 4.4101 0.1571 0.3272
## site27R 0.3276 3.0528 0.2657 0.4038
## site28R 0.2783 3.5939 0.2388 0.3242
## site29R 0.3248 3.0786 0.2401 0.4394
## site30R 0.2738 3.6525 0.1902 0.3941
## site31R 0.3592 2.7840 0.3181 0.4056
## site32R 0.4760 2.1008 0.3787 0.5983
## site33R 0.2709 3.6909 0.2100 0.3496
## site34R 0.3632 2.7533 0.2828 0.4665
## site35R 0.5490 1.8213 0.5027 0.5997
## site36R 0.2486 4.0222 0.2117 0.2920
## site37R 0.2544 3.9310 0.1888 0.3428
## site38R 0.3111 3.2142 0.2694 0.3593
## site39R 0.6184 1.6170 0.5342 0.7160
## site40R 0.4851 2.0614 0.3373 0.6977
## site41R 0.3626 2.7579 0.3018 0.4356
## site42R 0.4030 2.4815 0.3691 0.4400
##
## Concordance= 0.686 (se = 0.064 )
## Likelihood ratio test= 3187 on 59 df, p=<2e-16
## Wald test = 258.2 on 59 df, p=<2e-16
## Score (logrank) test = 2590 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
summary(All$SO4)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.1354 0.6529 1.4104 1.6701 2.1343 6.6991 688
IQR(All$SO4, na.rm=T)
## [1] 1.481359
# Will use the 5yr pre-censoring IQR (0.9653843), not this one
All <- All %>% mutate(SO4_IQR = SO4/0.9653843)
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ SO4_IQR + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SO4_IQR + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All,
## id = ID, cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## SO4_IQR 0.7227055 2.0599990 0.0221052 0.0774219 9.335 < 2e-16
## dx_yr 0.3436630 1.4101033 0.0070602 0.1252699 2.743 0.006081
## age_dx 0.0091231 1.0091649 0.0011770 0.0034138 2.672 0.007531
## sexF -0.1358048 0.8730131 0.0267003 0.0629412 -2.158 0.030955
## dich_RaceNon-White 0.0173323 1.0174833 0.0368078 0.0356521 0.486 0.626861
## smokeHxFormer 0.1214572 1.1291410 0.0328996 0.0232463 5.225 1.74e-07
## smokeHxAlways 0.0250676 1.0253845 0.0748671 0.0601250 0.417 0.676733
## smokeHxUnknown -0.0534333 0.9479691 0.0705105 0.0842372 -0.634 0.525872
## smokeHxEver -0.0001398 0.9998603 0.0498816 0.0146130 -0.010 0.992369
## disadv 0.0194098 1.0195994 0.0456339 0.0706709 0.275 0.783585
## site02R 0.1394609 1.1496539 0.2585339 0.1014674 1.374 0.169305
## site03R -0.4106006 0.6632518 0.2676881 0.1151425 -3.566 0.000362
## site04R -1.0460758 0.3513137 0.2762447 0.1211479 -8.635 < 2e-16
## site05R -1.4455201 0.2356235 0.2840259 0.0568837 -25.412 < 2e-16
## site06R -0.9516495 0.3861036 0.2742355 0.0962182 -9.891 < 2e-16
## site07R -1.3756514 0.2526750 0.2514679 0.1862686 -7.385 1.52e-13
## site09R -0.7416434 0.4763305 0.2761663 0.0673538 -11.011 < 2e-16
## site1 -1.5544149 0.2113130 0.2274027 0.0719897 -21.592 < 2e-16
## site101 -1.1335593 0.3218855 0.2282521 0.2010436 -5.638 1.72e-08
## site102 -0.6376679 0.5285236 0.2272866 0.1546209 -4.124 3.72e-05
## site103 -0.3352224 0.7151790 0.2227826 0.1082563 -3.097 0.001958
## site104 -0.6595980 0.5170592 0.2289029 0.1281763 -5.146 2.66e-07
## site105 -1.0161273 0.3619941 0.2257368 0.1881986 -5.399 6.69e-08
## site106 -1.3566176 0.2575304 0.2275374 0.1704373 -7.960 1.73e-15
## site107 -0.7176227 0.4879108 0.2481484 0.2087767 -3.437 0.000588
## site108 -0.6447068 0.5248164 0.2468598 0.2847586 -2.264 0.023571
## site10R -0.8852539 0.4126094 0.2970140 0.1527895 -5.794 6.88e-09
## site11R -1.1588617 0.3138432 0.2466684 0.1069997 -10.831 < 2e-16
## site12R -0.6236159 0.5360028 0.2572520 0.0365245 -17.074 < 2e-16
## site13R -1.4270846 0.2400076 0.2468433 0.1774411 -8.043 8.80e-16
## site14R -0.7228767 0.4853540 0.3994901 0.1160683 -6.228 4.72e-10
## site15R -0.8985221 0.4071710 0.2764243 0.0811261 -11.076 < 2e-16
## site16R -0.5418222 0.5816873 0.2681892 0.0413544 -13.102 < 2e-16
## site17R -1.0283076 0.3576117 0.2811913 0.0646268 -15.911 < 2e-16
## site18R -1.4565403 0.2330411 0.2657102 0.2465883 -5.907 3.49e-09
## site19R -0.2920568 0.7467261 0.2870797 0.0632030 -4.621 3.82e-06
## site20R -0.9920734 0.3708070 0.2922973 0.0635554 -15.610 < 2e-16
## site21R -1.3475211 0.2598837 0.2586169 0.0881674 -15.284 < 2e-16
## site22R -0.9842263 0.3737283 0.2532427 0.0724626 -13.583 < 2e-16
## site23R -1.0841697 0.3381825 0.2564349 0.0770784 -14.066 < 2e-16
## site24R -0.4111608 0.6628803 0.2539202 0.0319734 -12.859 < 2e-16
## site25R -0.4514899 0.6366789 0.2568174 0.1107135 -4.078 4.54e-05
## site26R -1.4838953 0.2267527 0.2720764 0.1871254 -7.930 2.19e-15
## site27R -1.1160514 0.3275707 0.3734863 0.1067333 -10.456 < 2e-16
## site28R -1.2792304 0.2782514 0.2729314 0.0779668 -16.407 < 2e-16
## site29R -1.1244751 0.3248229 0.3397708 0.1540943 -7.297 2.94e-13
## site30R -1.2954233 0.2737820 0.2618878 0.1858099 -6.972 3.13e-12
## site31R -1.0238891 0.3591953 0.2758866 0.0619911 -16.517 < 2e-16
## site32R -0.7423082 0.4760139 0.2781651 0.1166662 -6.363 1.98e-10
## site33R -1.3058681 0.2709372 0.2646573 0.1301099 -10.037 < 2e-16
## site34R -1.0127968 0.3632018 0.2538071 0.1276968 -7.931 2.17e-15
## site35R -0.5995747 0.5490451 0.2704420 0.0450046 -13.323 < 2e-16
## site36R -1.3918254 0.2486211 0.2612833 0.0821215 -16.948 < 2e-16
## site37R -1.3688891 0.2543894 0.2631513 0.1521625 -8.996 < 2e-16
## site38R -1.1675748 0.3111206 0.2678772 0.0735074 -15.884 < 2e-16
## site39R -0.4805553 0.6184399 0.2939729 0.0747571 -6.428 1.29e-10
## site40R -0.7233672 0.4851160 0.2719657 0.1854047 -3.902 9.56e-05
## site41R -1.0144742 0.3625930 0.2569589 0.0935923 -10.839 < 2e-16
## site42R -0.9088722 0.4029784 0.2810688 0.0448728 -20.254 < 2e-16
##
## SO4_IQR ***
## dx_yr **
## age_dx **
## sexF *
## dich_RaceNon-White
## smokeHxFormer ***
## smokeHxAlways
## smokeHxUnknown
## smokeHxEver
## disadv
## site02R
## site03R ***
## site04R ***
## site05R ***
## site06R ***
## site07R ***
## site09R ***
## site1 ***
## site101 ***
## site102 ***
## site103 **
## site104 ***
## site105 ***
## site106 ***
## site107 ***
## site108 *
## site10R ***
## site11R ***
## site12R ***
## site13R ***
## site14R ***
## site15R ***
## site16R ***
## site17R ***
## site18R ***
## site19R ***
## site20R ***
## site21R ***
## site22R ***
## site23R ***
## site24R ***
## site25R ***
## site26R ***
## site27R ***
## site28R ***
## site29R ***
## site30R ***
## site31R ***
## site32R ***
## site33R ***
## site34R ***
## site35R ***
## site36R ***
## site37R ***
## site38R ***
## site39R ***
## site40R ***
## site41R ***
## site42R ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SO4_IQR 2.0600 0.4854 1.7700 2.3976
## dx_yr 1.4101 0.7092 1.1031 1.8025
## age_dx 1.0092 0.9909 1.0024 1.0159
## sexF 0.8730 1.1455 0.7717 0.9876
## dich_RaceNon-White 1.0175 0.9828 0.9488 1.0911
## smokeHxFormer 1.1291 0.8856 1.0788 1.1818
## smokeHxAlways 1.0254 0.9752 0.9114 1.1536
## smokeHxUnknown 0.9480 1.0549 0.8037 1.1181
## smokeHxEver 0.9999 1.0001 0.9716 1.0289
## disadv 1.0196 0.9808 0.8877 1.1711
## site02R 1.1497 0.8698 0.9423 1.4026
## site03R 0.6633 1.5077 0.5293 0.8312
## site04R 0.3513 2.8465 0.2771 0.4455
## site05R 0.2356 4.2441 0.2108 0.2634
## site06R 0.3861 2.5900 0.3197 0.4662
## site07R 0.2527 3.9577 0.1754 0.3640
## site09R 0.4763 2.0994 0.4174 0.5436
## site1 0.2113 4.7323 0.1835 0.2433
## site101 0.3219 3.1067 0.2171 0.4773
## site102 0.5285 1.8921 0.3903 0.7156
## site103 0.7152 1.3983 0.5785 0.8842
## site104 0.5171 1.9340 0.4022 0.6647
## site105 0.3620 2.7625 0.2503 0.5235
## site106 0.2575 3.8830 0.1844 0.3597
## site107 0.4879 2.0496 0.3241 0.7346
## site108 0.5248 1.9054 0.3003 0.9171
## site10R 0.4126 2.4236 0.3058 0.5567
## site11R 0.3138 3.1863 0.2545 0.3871
## site12R 0.5360 1.8657 0.4990 0.5758
## site13R 0.2400 4.1665 0.1695 0.3398
## site14R 0.4854 2.0604 0.3866 0.6093
## site15R 0.4072 2.4560 0.3473 0.4773
## site16R 0.5817 1.7191 0.5364 0.6308
## site17R 0.3576 2.7963 0.3151 0.4059
## site18R 0.2330 4.2911 0.1437 0.3779
## site19R 0.7467 1.3392 0.6597 0.8452
## site20R 0.3708 2.6968 0.3274 0.4200
## site21R 0.2599 3.8479 0.2186 0.3089
## site22R 0.3737 2.6757 0.3242 0.4308
## site23R 0.3382 2.9570 0.2908 0.3933
## site24R 0.6629 1.5086 0.6226 0.7058
## site25R 0.6367 1.5707 0.5125 0.7910
## site26R 0.2268 4.4101 0.1571 0.3272
## site27R 0.3276 3.0528 0.2657 0.4038
## site28R 0.2783 3.5939 0.2388 0.3242
## site29R 0.3248 3.0786 0.2401 0.4394
## site30R 0.2738 3.6525 0.1902 0.3941
## site31R 0.3592 2.7840 0.3181 0.4056
## site32R 0.4760 2.1008 0.3787 0.5983
## site33R 0.2709 3.6909 0.2100 0.3496
## site34R 0.3632 2.7533 0.2828 0.4665
## site35R 0.5490 1.8213 0.5027 0.5997
## site36R 0.2486 4.0222 0.2117 0.2920
## site37R 0.2544 3.9310 0.1888 0.3428
## site38R 0.3111 3.2142 0.2694 0.3593
## site39R 0.6184 1.6170 0.5342 0.7160
## site40R 0.4851 2.0614 0.3373 0.6977
## site41R 0.3626 2.7579 0.3018 0.4356
## site42R 0.4030 2.4815 0.3691 0.4400
##
## Concordance= 0.686 (se = 0.064 )
## Likelihood ratio test= 3187 on 59 df, p=<2e-16
## Wald test = 258.2 on 59 df, p=<2e-16
## Score (logrank) test = 2590 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
So this indicates that there is a HR of 2.05 per IQR increase in SO4 as compared with a HR of 2.11 per 1ug/m3 increase in SO4.
Base model
#First need to make dataframe that only includes patients with a value for event
Allx <- All %>% filter(!is.na(SO4) & !is.na(deadORtx) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(cohort) & !is.na(site) & SO4<20)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$SO4, df=3) + Allx$dx_yr + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$SO4, exp(predicted$fit), type="n")
lines(sm.spline(Allx$SO4, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$SO4, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$SO4, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
Complete model
#First need to make dataframe that only includes patients with time_DeathTxCensor
Allx <- All %>% filter(!is.na(SO4) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(deadORtx) & !is.na(age_dx) & !is.na(sex) & !is.na(smokeHx) & !is.na(dich_Race) & !is.na(disadv) & !is.na(site) & SO4<20)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$SO4, df=3) + Allx$dx_yr + Allx$age_dx + Allx$sex + Allx$smokeHx + Allx$dich_Race + Allx$disadv + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$SO4, exp(predicted$fit), type="n")
lines(sm.spline(Allx$SO4, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$SO4, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$SO4, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ NO3 + dx_yr + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NO3 + dx_yr +
## site, data = All, id = ID, cluster = cohort)
##
## n= 335367, number of events= 6459
## (1981 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## NO3 -0.021509 0.978721 0.052338 0.106715 -0.202 0.84027
## dx_yr 0.218105 1.243717 0.005658 0.155595 1.402 0.16099
## site02R 0.492259 1.636008 0.257029 0.226539 2.173 0.02978 *
## site03R -0.211901 0.809044 0.263642 0.120585 -1.757 0.07887 .
## site04R -0.161510 0.850858 0.278215 0.208475 -0.775 0.43851
## site05R -0.169848 0.843793 0.278414 0.070846 -2.397 0.01651 *
## site06R 0.101265 1.106570 0.267093 0.073260 1.382 0.16689
## site07R -0.397046 0.672303 0.251428 0.295040 -1.346 0.17839
## site09R -0.062169 0.939724 0.270074 0.123793 -0.502 0.61553
## site1 -0.104759 0.900542 0.217478 0.158766 -0.660 0.50936
## site101 -0.359880 0.697760 0.222071 0.303268 -1.187 0.23536
## site102 -0.445886 0.640257 0.220307 0.229350 -1.944 0.05188 .
## site103 -0.164343 0.848451 0.215497 0.153011 -1.074 0.28280
## site104 -0.340593 0.711349 0.221274 0.170711 -1.995 0.04603 *
## site105 -0.313069 0.731200 0.218308 0.289843 -1.080 0.28008
## site106 -0.353308 0.702361 0.222203 0.276973 -1.276 0.20210
## site107 0.011366 1.011431 0.241472 0.327069 0.035 0.97228
## site108 -0.123338 0.883965 0.240910 0.463727 -0.266 0.79026
## site10R -0.250208 0.778639 0.289740 0.154203 -1.623 0.10468
## site11R -0.076466 0.926384 0.238429 0.100103 -0.764 0.44494
## site12R -0.097380 0.907211 0.255903 0.094582 -1.030 0.30320
## site13R -0.370677 0.690267 0.241987 0.235064 -1.577 0.11481
## site14R 0.135180 1.144743 0.398037 0.097536 1.386 0.16576
## site15R 0.024692 1.024999 0.270647 0.058370 0.423 0.67227
## site16R 0.157296 1.170342 0.265234 0.023727 6.629 3.37e-11 ***
## site17R -0.049507 0.951699 0.278571 0.106378 -0.465 0.64166
## site18R -0.436036 0.646595 0.259487 0.294133 -1.482 0.13822
## site19R -0.090277 0.913678 0.282857 0.051374 -1.757 0.07888 .
## site20R -0.207490 0.812621 0.289565 0.087297 -2.377 0.01746 *
## site21R -0.281511 0.754642 0.252426 0.053142 -5.297 1.17e-07 ***
## site22R -0.020363 0.979842 0.251067 0.138222 -0.147 0.88288
## site23R -0.144319 0.865612 0.248754 0.073816 -1.955 0.05057 .
## site24R 0.040953 1.041803 0.254214 0.044151 0.928 0.35364
## site25R -0.093084 0.911117 0.256622 0.220754 -0.422 0.67327
## site26R -0.353691 0.702092 0.265547 0.146109 -2.421 0.01549 *
## site27R -0.147198 0.863123 0.370411 0.124580 -1.182 0.23738
## site28R -0.031776 0.968724 0.269727 0.143530 -0.221 0.82479
## site29R -0.324466 0.722913 0.335573 0.154830 -2.096 0.03611 *
## site30R -0.250547 0.778375 0.265243 0.320008 -0.783 0.43366
## site31R -0.191453 0.825758 0.270714 0.071143 -2.691 0.00712 **
## site32R -0.418893 0.657775 0.268177 0.158640 -2.641 0.00828 **
## site33R -0.287528 0.750115 0.257624 0.102005 -2.819 0.00482 **
## site34R -0.207735 0.812422 0.248795 0.153014 -1.358 0.17458
## site35R -0.071443 0.931050 0.264357 0.067425 -1.060 0.28933
## site36R -0.198459 0.819993 0.259579 0.138344 -1.435 0.15142
## site37R -0.223170 0.799979 0.261880 0.247253 -0.903 0.36674
## site38R -0.278244 0.757112 0.261180 0.070099 -3.969 7.21e-05 ***
## site39R -0.248342 0.780093 0.291634 0.157763 -1.574 0.11545
## site40R 0.082887 1.086419 0.285975 0.433906 0.191 0.84851
## site41R -0.248450 0.780009 0.254794 0.175858 -1.413 0.15772
## site42R 0.150365 1.162259 0.277853 0.063031 2.386 0.01705 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NO3 0.9787 1.0217 0.7940 1.2064
## dx_yr 1.2437 0.8040 0.9168 1.6872
## site02R 1.6360 0.6112 1.0494 2.5504
## site03R 0.8090 1.2360 0.6387 1.0247
## site04R 0.8509 1.1753 0.5655 1.2803
## site05R 0.8438 1.1851 0.7344 0.9695
## site06R 1.1066 0.9037 0.9586 1.2774
## site07R 0.6723 1.4874 0.3771 1.1987
## site09R 0.9397 1.0641 0.7373 1.1978
## site1 0.9005 1.1104 0.6597 1.2293
## site101 0.6978 1.4332 0.3851 1.2643
## site102 0.6403 1.5619 0.4084 1.0036
## site103 0.8485 1.1786 0.6286 1.1452
## site104 0.7113 1.4058 0.5091 0.9940
## site105 0.7312 1.3676 0.4143 1.2905
## site106 0.7024 1.4238 0.4081 1.2087
## site107 1.0114 0.9887 0.5328 1.9202
## site108 0.8840 1.1313 0.3562 2.1936
## site10R 0.7786 1.2843 0.5755 1.0534
## site11R 0.9264 1.0795 0.7613 1.1272
## site12R 0.9072 1.1023 0.7537 1.0920
## site13R 0.6903 1.4487 0.4354 1.0942
## site14R 1.1447 0.8736 0.9456 1.3859
## site15R 1.0250 0.9756 0.9142 1.1492
## site16R 1.1703 0.8545 1.1172 1.2261
## site17R 0.9517 1.0508 0.7726 1.1723
## site18R 0.6466 1.5466 0.3633 1.1508
## site19R 0.9137 1.0945 0.8262 1.0105
## site20R 0.8126 1.2306 0.6848 0.9643
## site21R 0.7546 1.3251 0.6800 0.8375
## site22R 0.9798 1.0206 0.7473 1.2847
## site23R 0.8656 1.1553 0.7490 1.0004
## site24R 1.0418 0.9599 0.9554 1.1360
## site25R 0.9111 1.0976 0.5911 1.4044
## site26R 0.7021 1.4243 0.5273 0.9349
## site27R 0.8631 1.1586 0.6761 1.1018
## site28R 0.9687 1.0323 0.7312 1.2834
## site29R 0.7229 1.3833 0.5337 0.9792
## site30R 0.7784 1.2847 0.4157 1.4574
## site31R 0.8258 1.2110 0.7183 0.9493
## site32R 0.6578 1.5203 0.4820 0.8977
## site33R 0.7501 1.3331 0.6142 0.9161
## site34R 0.8124 1.2309 0.6019 1.0965
## site35R 0.9310 1.0741 0.8158 1.0626
## site36R 0.8200 1.2195 0.6252 1.0754
## site37R 0.8000 1.2500 0.4927 1.2988
## site38R 0.7571 1.3208 0.6599 0.8686
## site39R 0.7801 1.2819 0.5726 1.0628
## site40R 1.0864 0.9205 0.4641 2.5430
## site41R 0.7800 1.2820 0.5526 1.1010
## site42R 1.1623 0.8604 1.0272 1.3151
##
## Concordance= 0.673 (se = 0.067 )
## Likelihood ratio test= 2368 on 51 df, p=<2e-16
## Wald test = 13.79 on 51 df, p=1
## Score (logrank) test = 1909 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ NO3 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NO3 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All,
## id = ID, cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## NO3 -0.008507 0.991529 0.053139 0.114125 -0.075 0.940582
## dx_yr 0.215262 1.240186 0.005823 0.159008 1.354 0.175808
## age_dx 0.009958 1.010007 0.001177 0.003977 2.504 0.012295 *
## sexF -0.141777 0.867815 0.026687 0.061665 -2.299 0.021497 *
## dich_RaceNon-White 0.005301 1.005315 0.036768 0.036088 0.147 0.883226
## smokeHxFormer 0.105323 1.111070 0.032876 0.054535 1.931 0.053446 .
## smokeHxAlways 0.047988 1.049158 0.074817 0.023847 2.012 0.044187 *
## smokeHxUnknown 0.061439 1.063365 0.070542 0.038548 1.594 0.110973
## smokeHxEver -0.006549 0.993472 0.049914 0.012427 -0.527 0.598170
## disadv 0.005159 1.005172 0.045473 0.047703 0.108 0.913885
## site02R 0.465406 1.592661 0.261527 0.228983 2.032 0.042104 *
## site03R -0.201467 0.817531 0.267979 0.122596 -1.643 0.100312
## site04R -0.129377 0.878643 0.282780 0.233514 -0.554 0.579550
## site05R -0.149882 0.860809 0.284467 0.060909 -2.461 0.013865 *
## site06R 0.066749 1.069028 0.273000 0.102670 0.650 0.515605
## site07R -0.448004 0.638902 0.256082 0.291040 -1.539 0.123727
## site09R -0.113719 0.892509 0.276333 0.108040 -1.053 0.292540
## site1 -0.169597 0.844005 0.225101 0.142391 -1.191 0.233628
## site101 -0.380467 0.683542 0.229063 0.286450 -1.328 0.184106
## site102 -0.486239 0.614935 0.227491 0.209996 -2.315 0.020588 *
## site103 -0.196553 0.821558 0.222663 0.119326 -1.647 0.099519 .
## site104 -0.395691 0.673215 0.228217 0.132492 -2.987 0.002822 **
## site105 -0.365534 0.693826 0.225846 0.266183 -1.373 0.169677
## site106 -0.460711 0.630835 0.229561 0.242425 -1.900 0.057377 .
## site107 -0.051238 0.950053 0.248162 0.299594 -0.171 0.864205
## site108 -0.191710 0.825546 0.247357 0.433422 -0.442 0.658259
## site10R -0.297053 0.743005 0.296541 0.143386 -2.072 0.038293 *
## site11R -0.137943 0.871148 0.244817 0.088189 -1.564 0.117776
## site12R -0.143489 0.866330 0.260613 0.092045 -1.559 0.119018
## site13R -0.452695 0.635912 0.246686 0.226880 -1.995 0.046010 *
## site14R 0.137957 1.147926 0.400917 0.103911 1.328 0.184295
## site15R -0.038092 0.962625 0.275257 0.049682 -0.767 0.443251
## site16R 0.173553 1.189524 0.269432 0.039229 4.424 9.69e-06 ***
## site17R -0.014330 0.985773 0.282993 0.122206 -0.117 0.906655
## site18R -0.474896 0.621950 0.264106 0.276282 -1.719 0.085636 .
## site19R -0.128958 0.879011 0.287121 0.036472 -3.536 0.000406 ***
## site20R -0.231198 0.793582 0.293837 0.092520 -2.499 0.012458 *
## site21R -0.321606 0.724984 0.256910 0.042597 -7.550 4.35e-14 ***
## site22R -0.080358 0.922786 0.255870 0.135310 -0.594 0.552593
## site23R -0.172176 0.841831 0.255531 0.054615 -3.153 0.001619 **
## site24R 0.057019 1.058675 0.259085 0.067855 0.840 0.400738
## site25R -0.130761 0.877428 0.263101 0.227869 -0.574 0.566074
## site26R -0.407310 0.665438 0.270116 0.135366 -3.009 0.002622 **
## site27R -0.136854 0.872098 0.373494 0.130238 -1.051 0.293350
## site28R 0.030488 1.030957 0.274244 0.156947 0.194 0.845978
## site29R -0.361106 0.696905 0.339663 0.161800 -2.232 0.025628 *
## site30R -0.316432 0.728744 0.270691 0.320053 -0.989 0.322816
## site31R -0.188192 0.828455 0.274849 0.071589 -2.629 0.008569 **
## site32R -0.415412 0.660069 0.278171 0.146648 -2.833 0.004615 **
## site33R -0.327836 0.720481 0.263074 0.079675 -4.115 3.88e-05 ***
## site34R -0.252667 0.776726 0.253962 0.148488 -1.702 0.088832 .
## site35R -0.124583 0.882865 0.270182 0.050038 -2.490 0.012782 *
## site36R -0.208284 0.811976 0.264184 0.139149 -1.497 0.134433
## site37R -0.275480 0.759208 0.266388 0.247827 -1.112 0.266319
## site38R -0.300539 0.740419 0.266683 0.066902 -4.492 7.05e-06 ***
## site39R -0.305335 0.736876 0.295957 0.144781 -2.109 0.034949 *
## site40R 0.009934 1.009983 0.292066 0.429334 0.023 0.981541
## site41R -0.264493 0.767595 0.259202 0.177673 -1.489 0.136579
## site42R 0.143293 1.154068 0.281946 0.070257 2.040 0.041396 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NO3 0.9915 1.0085 0.7928 1.2401
## dx_yr 1.2402 0.8063 0.9081 1.6937
## age_dx 1.0100 0.9901 1.0022 1.0179
## sexF 0.8678 1.1523 0.7690 0.9793
## dich_RaceNon-White 1.0053 0.9947 0.9367 1.0790
## smokeHxFormer 1.1111 0.9000 0.9984 1.2364
## smokeHxAlways 1.0492 0.9531 1.0012 1.0994
## smokeHxUnknown 1.0634 0.9404 0.9860 1.1468
## smokeHxEver 0.9935 1.0066 0.9696 1.0180
## disadv 1.0052 0.9949 0.9155 1.1037
## site02R 1.5927 0.6279 1.0167 2.4948
## site03R 0.8175 1.2232 0.6429 1.0396
## site04R 0.8786 1.1381 0.5560 1.3886
## site05R 0.8608 1.1617 0.7639 0.9700
## site06R 1.0690 0.9354 0.8742 1.3073
## site07R 0.6389 1.5652 0.3612 1.1302
## site09R 0.8925 1.1204 0.7222 1.1030
## site1 0.8440 1.1848 0.6385 1.1157
## site101 0.6835 1.4630 0.3899 1.1984
## site102 0.6149 1.6262 0.4075 0.9281
## site103 0.8216 1.2172 0.6502 1.0380
## site104 0.6732 1.4854 0.5192 0.8728
## site105 0.6938 1.4413 0.4118 1.1690
## site106 0.6308 1.5852 0.3922 1.0145
## site107 0.9501 1.0526 0.5281 1.7091
## site108 0.8255 1.2113 0.3530 1.9305
## site10R 0.7430 1.3459 0.5610 0.9841
## site11R 0.8711 1.1479 0.7329 1.0355
## site12R 0.8663 1.1543 0.7233 1.0376
## site13R 0.6359 1.5725 0.4076 0.9920
## site14R 1.1479 0.8711 0.9364 1.4072
## site15R 0.9626 1.0388 0.8733 1.0611
## site16R 1.1895 0.8407 1.1015 1.2846
## site17R 0.9858 1.0144 0.7758 1.2526
## site18R 0.6219 1.6078 0.3619 1.0689
## site19R 0.8790 1.1376 0.8184 0.9441
## site20R 0.7936 1.2601 0.6620 0.9514
## site21R 0.7250 1.3793 0.6669 0.7881
## site22R 0.9228 1.0837 0.7078 1.2030
## site23R 0.8418 1.1879 0.7564 0.9369
## site24R 1.0587 0.9446 0.9268 1.2093
## site25R 0.8774 1.1397 0.5614 1.3714
## site26R 0.6654 1.5028 0.5104 0.8676
## site27R 0.8721 1.1467 0.6756 1.1257
## site28R 1.0310 0.9700 0.7580 1.4023
## site29R 0.6969 1.4349 0.5075 0.9570
## site30R 0.7287 1.3722 0.3892 1.3646
## site31R 0.8285 1.2071 0.7200 0.9532
## site32R 0.6601 1.5150 0.4952 0.8799
## site33R 0.7205 1.3880 0.6163 0.8423
## site34R 0.7767 1.2875 0.5806 1.0391
## site35R 0.8829 1.1327 0.8004 0.9738
## site36R 0.8120 1.2316 0.6182 1.0666
## site37R 0.7592 1.3172 0.4671 1.2340
## site38R 0.7404 1.3506 0.6494 0.8442
## site39R 0.7369 1.3571 0.5548 0.9787
## site40R 1.0100 0.9901 0.4354 2.3430
## site41R 0.7676 1.3028 0.5419 1.0873
## site42R 1.1541 0.8665 1.0056 1.3244
##
## Concordance= 0.676 (se = 0.064 )
## Likelihood ratio test= 2535 on 59 df, p=<2e-16
## Wald test = 12.32 on 59 df, p=1
## Score (logrank) test = 2067 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
summary(All$NO3)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0134 0.4777 0.8362 0.8433 1.1149 4.7595 688
IQR(All$NO3, na.rm=T)
## [1] 0.6372749
# Will use the 5yr pre-censoring IQR (0.5319728), not this one
All <- All %>% mutate(NO3_IQR = NO3/0.5319728)
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ NO3_IQR + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NO3_IQR + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All,
## id = ID, cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## NO3_IQR -0.004525 0.995485 0.028268 0.060712 -0.075 0.940582
## dx_yr 0.215262 1.240186 0.005823 0.159008 1.354 0.175808
## age_dx 0.009958 1.010007 0.001177 0.003977 2.504 0.012295 *
## sexF -0.141777 0.867815 0.026687 0.061665 -2.299 0.021497 *
## dich_RaceNon-White 0.005301 1.005315 0.036768 0.036088 0.147 0.883226
## smokeHxFormer 0.105323 1.111070 0.032876 0.054535 1.931 0.053446 .
## smokeHxAlways 0.047988 1.049158 0.074817 0.023847 2.012 0.044187 *
## smokeHxUnknown 0.061439 1.063365 0.070542 0.038548 1.594 0.110973
## smokeHxEver -0.006549 0.993472 0.049914 0.012427 -0.527 0.598170
## disadv 0.005159 1.005172 0.045473 0.047703 0.108 0.913885
## site02R 0.465406 1.592661 0.261527 0.228983 2.032 0.042104 *
## site03R -0.201467 0.817531 0.267979 0.122596 -1.643 0.100312
## site04R -0.129377 0.878643 0.282780 0.233514 -0.554 0.579550
## site05R -0.149882 0.860809 0.284467 0.060909 -2.461 0.013865 *
## site06R 0.066749 1.069028 0.273000 0.102670 0.650 0.515605
## site07R -0.448004 0.638902 0.256082 0.291040 -1.539 0.123727
## site09R -0.113719 0.892509 0.276333 0.108040 -1.053 0.292540
## site1 -0.169597 0.844005 0.225101 0.142391 -1.191 0.233628
## site101 -0.380467 0.683542 0.229063 0.286450 -1.328 0.184106
## site102 -0.486239 0.614935 0.227491 0.209996 -2.315 0.020588 *
## site103 -0.196553 0.821558 0.222663 0.119326 -1.647 0.099519 .
## site104 -0.395691 0.673215 0.228217 0.132492 -2.987 0.002822 **
## site105 -0.365534 0.693826 0.225846 0.266183 -1.373 0.169677
## site106 -0.460711 0.630835 0.229561 0.242425 -1.900 0.057377 .
## site107 -0.051238 0.950053 0.248162 0.299594 -0.171 0.864205
## site108 -0.191710 0.825546 0.247357 0.433422 -0.442 0.658259
## site10R -0.297053 0.743005 0.296541 0.143386 -2.072 0.038293 *
## site11R -0.137943 0.871148 0.244817 0.088189 -1.564 0.117776
## site12R -0.143489 0.866330 0.260613 0.092045 -1.559 0.119018
## site13R -0.452695 0.635912 0.246686 0.226880 -1.995 0.046010 *
## site14R 0.137957 1.147926 0.400917 0.103911 1.328 0.184295
## site15R -0.038092 0.962625 0.275257 0.049682 -0.767 0.443251
## site16R 0.173553 1.189524 0.269432 0.039229 4.424 9.69e-06 ***
## site17R -0.014330 0.985773 0.282993 0.122206 -0.117 0.906655
## site18R -0.474896 0.621950 0.264106 0.276282 -1.719 0.085636 .
## site19R -0.128958 0.879011 0.287121 0.036472 -3.536 0.000406 ***
## site20R -0.231198 0.793582 0.293837 0.092520 -2.499 0.012458 *
## site21R -0.321606 0.724984 0.256910 0.042597 -7.550 4.35e-14 ***
## site22R -0.080358 0.922786 0.255870 0.135310 -0.594 0.552593
## site23R -0.172176 0.841831 0.255531 0.054615 -3.153 0.001619 **
## site24R 0.057019 1.058675 0.259085 0.067855 0.840 0.400738
## site25R -0.130761 0.877428 0.263101 0.227869 -0.574 0.566074
## site26R -0.407310 0.665438 0.270116 0.135366 -3.009 0.002622 **
## site27R -0.136854 0.872098 0.373494 0.130238 -1.051 0.293350
## site28R 0.030488 1.030957 0.274244 0.156947 0.194 0.845978
## site29R -0.361106 0.696905 0.339663 0.161800 -2.232 0.025628 *
## site30R -0.316432 0.728744 0.270691 0.320053 -0.989 0.322816
## site31R -0.188192 0.828455 0.274849 0.071589 -2.629 0.008569 **
## site32R -0.415412 0.660069 0.278171 0.146648 -2.833 0.004615 **
## site33R -0.327836 0.720481 0.263074 0.079675 -4.115 3.88e-05 ***
## site34R -0.252667 0.776726 0.253962 0.148488 -1.702 0.088832 .
## site35R -0.124583 0.882865 0.270182 0.050038 -2.490 0.012782 *
## site36R -0.208284 0.811976 0.264184 0.139149 -1.497 0.134433
## site37R -0.275480 0.759208 0.266388 0.247827 -1.112 0.266319
## site38R -0.300539 0.740419 0.266683 0.066902 -4.492 7.05e-06 ***
## site39R -0.305335 0.736876 0.295957 0.144781 -2.109 0.034949 *
## site40R 0.009934 1.009983 0.292066 0.429334 0.023 0.981541
## site41R -0.264493 0.767595 0.259202 0.177673 -1.489 0.136579
## site42R 0.143293 1.154068 0.281946 0.070257 2.040 0.041396 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NO3_IQR 0.9955 1.0045 0.8838 1.1213
## dx_yr 1.2402 0.8063 0.9081 1.6937
## age_dx 1.0100 0.9901 1.0022 1.0179
## sexF 0.8678 1.1523 0.7690 0.9793
## dich_RaceNon-White 1.0053 0.9947 0.9367 1.0790
## smokeHxFormer 1.1111 0.9000 0.9984 1.2364
## smokeHxAlways 1.0492 0.9531 1.0012 1.0994
## smokeHxUnknown 1.0634 0.9404 0.9860 1.1468
## smokeHxEver 0.9935 1.0066 0.9696 1.0180
## disadv 1.0052 0.9949 0.9155 1.1037
## site02R 1.5927 0.6279 1.0167 2.4948
## site03R 0.8175 1.2232 0.6429 1.0396
## site04R 0.8786 1.1381 0.5560 1.3886
## site05R 0.8608 1.1617 0.7639 0.9700
## site06R 1.0690 0.9354 0.8742 1.3073
## site07R 0.6389 1.5652 0.3612 1.1302
## site09R 0.8925 1.1204 0.7222 1.1030
## site1 0.8440 1.1848 0.6385 1.1157
## site101 0.6835 1.4630 0.3899 1.1984
## site102 0.6149 1.6262 0.4075 0.9281
## site103 0.8216 1.2172 0.6502 1.0380
## site104 0.6732 1.4854 0.5192 0.8728
## site105 0.6938 1.4413 0.4118 1.1690
## site106 0.6308 1.5852 0.3922 1.0145
## site107 0.9501 1.0526 0.5281 1.7091
## site108 0.8255 1.2113 0.3530 1.9305
## site10R 0.7430 1.3459 0.5610 0.9841
## site11R 0.8711 1.1479 0.7329 1.0355
## site12R 0.8663 1.1543 0.7233 1.0376
## site13R 0.6359 1.5725 0.4076 0.9920
## site14R 1.1479 0.8711 0.9364 1.4072
## site15R 0.9626 1.0388 0.8733 1.0611
## site16R 1.1895 0.8407 1.1015 1.2846
## site17R 0.9858 1.0144 0.7758 1.2526
## site18R 0.6219 1.6078 0.3619 1.0689
## site19R 0.8790 1.1376 0.8184 0.9441
## site20R 0.7936 1.2601 0.6620 0.9514
## site21R 0.7250 1.3793 0.6669 0.7881
## site22R 0.9228 1.0837 0.7078 1.2030
## site23R 0.8418 1.1879 0.7564 0.9369
## site24R 1.0587 0.9446 0.9268 1.2093
## site25R 0.8774 1.1397 0.5614 1.3714
## site26R 0.6654 1.5028 0.5104 0.8676
## site27R 0.8721 1.1467 0.6756 1.1257
## site28R 1.0310 0.9700 0.7580 1.4023
## site29R 0.6969 1.4349 0.5075 0.9570
## site30R 0.7287 1.3722 0.3892 1.3646
## site31R 0.8285 1.2071 0.7200 0.9532
## site32R 0.6601 1.5150 0.4952 0.8799
## site33R 0.7205 1.3880 0.6163 0.8423
## site34R 0.7767 1.2875 0.5806 1.0391
## site35R 0.8829 1.1327 0.8004 0.9738
## site36R 0.8120 1.2316 0.6182 1.0666
## site37R 0.7592 1.3172 0.4671 1.2340
## site38R 0.7404 1.3506 0.6494 0.8442
## site39R 0.7369 1.3571 0.5548 0.9787
## site40R 1.0100 0.9901 0.4354 2.3430
## site41R 0.7676 1.3028 0.5419 1.0873
## site42R 1.1541 0.8665 1.0056 1.3244
##
## Concordance= 0.676 (se = 0.064 )
## Likelihood ratio test= 2535 on 59 df, p=<2e-16
## Wald test = 12.32 on 59 df, p=1
## Score (logrank) test = 2067 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
So this indicates that there is a HR of 0.99 per IQR increase in NO3 as compared with a HR of 0.99 per 1ug/m3 increase in NO3.
Base model
#First need to make dataframe that only includes patients with a value for event
Allx <- All %>% filter(!is.na(NO3) & !is.na(deadORtx) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(cohort) & !is.na(site) & NO3<20)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$NO3, df=3) + Allx$dx_yr + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$NO3, exp(predicted$fit), type="n")
lines(sm.spline(Allx$NO3, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$NO3, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$NO3, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
Complete model
#First need to make dataframe that only includes patients with time_DeathTxCensor
Allx <- All %>% filter(!is.na(NO3) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(deadORtx) & !is.na(age_dx) & !is.na(sex) & !is.na(smokeHx) & !is.na(dich_Race) & !is.na(disadv) & !is.na(site) & NO3<20)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$NO3, df=3) + Allx$dx_yr + Allx$age_dx + Allx$sex + Allx$smokeHx + Allx$dich_Race + Allx$disadv + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$NO3, exp(predicted$fit), type="n")
lines(sm.spline(Allx$NO3, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$NO3, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$NO3, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ NH4 + dx_yr + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NH4 + dx_yr +
## site, data = All, id = ID, cluster = cohort)
##
## n= 335367, number of events= 6459
## (1981 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## NH4 1.671858 5.322045 0.078078 0.789905 2.117 0.034300 *
## dx_yr 0.349796 1.418779 0.008574 0.182352 1.918 0.055080 .
## site02R 0.015702 1.015826 0.254970 0.318107 0.049 0.960631
## site03R -0.407128 0.665559 0.263394 0.177984 -2.287 0.022170 *
## site04R -1.149122 0.316915 0.274386 0.426498 -2.694 0.007053 **
## site05R -1.256124 0.284756 0.279734 0.407766 -3.081 0.002067 **
## site06R -0.514479 0.597812 0.268206 0.152451 -3.375 0.000739 ***
## site07R -1.364070 0.255618 0.249266 0.541877 -2.517 0.011826 *
## site09R -0.582663 0.558409 0.270301 0.233206 -2.498 0.012472 *
## site1 -1.270590 0.280666 0.222627 0.516141 -2.462 0.013828 *
## site101 -1.053338 0.348771 0.222486 0.478951 -2.199 0.027859 *
## site102 -0.635929 0.529443 0.220224 0.229006 -2.777 0.005488 **
## site103 -0.207757 0.812405 0.215538 0.159307 -1.304 0.192190
## site104 -0.412959 0.661690 0.221325 0.173762 -2.377 0.017474 *
## site105 -0.891426 0.410071 0.218940 0.412888 -2.159 0.030850 *
## site106 -1.230615 0.292113 0.222255 0.510115 -2.412 0.015847 *
## site107 -0.583263 0.558075 0.242190 0.442516 -1.318 0.187484
## site108 -0.553043 0.575197 0.240919 0.476934 -1.160 0.246220
## site10R -0.316924 0.728386 0.289772 0.163726 -1.936 0.052905 .
## site11R -0.450807 0.637114 0.238993 0.200705 -2.246 0.024697 *
## site12R -0.694032 0.499558 0.253733 0.225550 -3.077 0.002090 **
## site13R -1.094469 0.334717 0.242708 0.418865 -2.613 0.008977 **
## site14R -0.781498 0.457720 0.398287 0.220497 -3.544 0.000394 ***
## site15R -0.206152 0.813710 0.270828 0.115281 -1.788 0.073736 .
## site16R -0.475886 0.621335 0.264706 0.177679 -2.678 0.007399 **
## site17R -0.897244 0.407692 0.277932 0.352543 -2.545 0.010926 *
## site18R -0.852169 0.426489 0.260123 0.382293 -2.229 0.025807 *
## site19R -0.072818 0.929770 0.282761 0.061768 -1.179 0.238437
## site20R -0.835086 0.433837 0.288569 0.242828 -3.439 0.000584 ***
## site21R -0.702296 0.495447 0.252989 0.171608 -4.092 4.27e-05 ***
## site22R -0.918628 0.399066 0.250665 0.363929 -2.524 0.011596 *
## site23R -0.671166 0.511112 0.249262 0.226980 -2.957 0.003107 **
## site24R -0.627308 0.534027 0.250465 0.179728 -3.490 0.000482 ***
## site25R -0.792201 0.452847 0.253472 0.464712 -1.705 0.088247 .
## site26R -0.914951 0.400536 0.266795 0.366534 -2.496 0.012552 *
## site27R -0.919708 0.398635 0.370966 0.370671 -2.481 0.013094 *
## site28R -1.165767 0.311684 0.270463 0.469547 -2.483 0.013037 *
## site29R -0.873819 0.417355 0.335746 0.289868 -3.015 0.002574 **
## site30R -1.327709 0.265084 0.259744 0.540400 -2.457 0.014014 *
## site31R -0.658991 0.517373 0.271421 0.206701 -3.188 0.001432 **
## site32R -0.650542 0.521763 0.268253 0.228661 -2.845 0.004441 **
## site33R -0.825359 0.438078 0.258706 0.309252 -2.669 0.007610 **
## site34R -0.861920 0.422350 0.249408 0.344729 -2.500 0.012409 *
## site35R -0.367377 0.692549 0.264593 0.128078 -2.868 0.004126 **
## site36R -1.373731 0.253161 0.259917 0.480864 -2.857 0.004279 **
## site37R -1.341163 0.261541 0.261838 0.534750 -2.508 0.012141 *
## site38R -0.672289 0.510539 0.261710 0.177859 -3.780 0.000157 ***
## site39R -0.616723 0.539710 0.290216 0.233117 -2.646 0.008156 **
## site40R -1.302602 0.271824 0.273611 0.788581 -1.652 0.098569 .
## site41R -0.923654 0.397066 0.253462 0.308677 -2.992 0.002769 **
## site42R -0.742172 0.476079 0.278205 0.318720 -2.329 0.019880 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NH4 5.3220 0.1879 1.13165 25.0292
## dx_yr 1.4188 0.7048 0.99242 2.0283
## site02R 1.0158 0.9844 0.54456 1.8949
## site03R 0.6656 1.5025 0.46956 0.9434
## site04R 0.3169 3.1554 0.13737 0.7311
## site05R 0.2848 3.5118 0.12805 0.6332
## site06R 0.5978 1.6728 0.44340 0.8060
## site07R 0.2556 3.9121 0.08838 0.7393
## site09R 0.5584 1.7908 0.35355 0.8820
## site1 0.2807 3.5630 0.10206 0.7718
## site101 0.3488 2.8672 0.13641 0.8917
## site102 0.5294 1.8888 0.33798 0.8294
## site103 0.8124 1.2309 0.59452 1.1101
## site104 0.6617 1.5113 0.47070 0.9302
## site105 0.4101 2.4386 0.18256 0.9211
## site106 0.2921 3.4233 0.10748 0.7939
## site107 0.5581 1.7919 0.23443 1.3285
## site108 0.5752 1.7385 0.22587 1.4648
## site10R 0.7284 1.3729 0.52844 1.0040
## site11R 0.6371 1.5696 0.42991 0.9442
## site12R 0.4996 2.0018 0.32107 0.7773
## site13R 0.3347 2.9876 0.14728 0.7607
## site14R 0.4577 2.1847 0.29711 0.7052
## site15R 0.8137 1.2289 0.64914 1.0200
## site16R 0.6213 1.6094 0.43862 0.8802
## site17R 0.4077 2.4528 0.20429 0.8136
## site18R 0.4265 2.3447 0.20160 0.9022
## site19R 0.9298 1.0755 0.82376 1.0494
## site20R 0.4338 2.3050 0.26955 0.6983
## site21R 0.4954 2.0184 0.35394 0.6935
## site22R 0.3991 2.5058 0.19555 0.8144
## site23R 0.5111 1.9565 0.32757 0.7975
## site24R 0.5340 1.8726 0.37547 0.7595
## site25R 0.4528 2.2083 0.18213 1.1259
## site26R 0.4005 2.4967 0.19528 0.8216
## site27R 0.3986 2.5086 0.19278 0.8243
## site28R 0.3117 3.2084 0.12418 0.7823
## site29R 0.4174 2.3960 0.23647 0.7366
## site30R 0.2651 3.7724 0.09192 0.7645
## site31R 0.5174 1.9328 0.34503 0.7758
## site32R 0.5218 1.9166 0.33330 0.8168
## site33R 0.4381 2.2827 0.23895 0.8031
## site34R 0.4224 2.3677 0.21490 0.8301
## site35R 0.6925 1.4439 0.53880 0.8902
## site36R 0.2532 3.9501 0.09865 0.6497
## site37R 0.2615 3.8235 0.09170 0.7460
## site38R 0.5105 1.9587 0.36028 0.7235
## site39R 0.5397 1.8528 0.34177 0.8523
## site40R 0.2718 3.6789 0.05795 1.2751
## site41R 0.3971 2.5185 0.21683 0.7271
## site42R 0.4761 2.1005 0.25491 0.8891
##
## Concordance= 0.674 (se = 0.065 )
## Likelihood ratio test= 2797 on 51 df, p=<2e-16
## Wald test = 6.45 on 51 df, p=1
## Score (logrank) test = 2202 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ NH4 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NH4 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All,
## id = ID, cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## NH4 1.7096909 5.5272525 0.0794455 0.7768404 2.201 0.027748
## dx_yr 0.3493734 1.4181786 0.0087555 0.1836184 1.903 0.057078
## age_dx 0.0097076 1.0097549 0.0011748 0.0036607 2.652 0.008006
## sexF -0.1376702 0.8713860 0.0266937 0.0592741 -2.323 0.020201
## dich_RaceNon-White 0.0106919 1.0107492 0.0367194 0.0274967 0.389 0.697393
## smokeHxFormer 0.0914987 1.0958154 0.0328697 0.0395747 2.312 0.020775
## smokeHxAlways -0.0001908 0.9998093 0.0748577 0.0395371 -0.005 0.996150
## smokeHxUnknown -0.0520493 0.9492820 0.0706821 0.0257670 -2.020 0.043383
## smokeHxEver -0.0095266 0.9905187 0.0499679 0.0146788 -0.649 0.516336
## disadv 0.0151891 1.0153050 0.0453374 0.0571111 0.266 0.790273
## site02R 0.0061263 1.0061451 0.2593874 0.3018780 0.020 0.983809
## site03R -0.3749202 0.6873441 0.2677471 0.1632482 -2.297 0.021640
## site04R -1.1024364 0.3320611 0.2785575 0.4081305 -2.701 0.006909
## site05R -1.2228944 0.2943769 0.2856961 0.3595665 -3.401 0.000671
## site06R -0.5365883 0.5847398 0.2740228 0.1053477 -5.094 3.52e-07
## site07R -1.3979246 0.2471093 0.2537142 0.5124788 -2.728 0.006376
## site09R -0.6220183 0.5368598 0.2765829 0.2086381 -2.981 0.002870
## site1 -1.3106019 0.2696577 0.2295537 0.4672318 -2.805 0.005031
## site101 -1.0604423 0.3463026 0.2291769 0.4387317 -2.417 0.015646
## site102 -0.6465331 0.5238588 0.2273540 0.2015462 -3.208 0.001337
## site103 -0.2181307 0.8040203 0.2227175 0.1227607 -1.777 0.075588
## site104 -0.4467383 0.6397113 0.2282395 0.1333780 -3.349 0.000810
## site105 -0.9235732 0.3970976 0.2262424 0.3682674 -2.508 0.012146
## site106 -1.3192127 0.2673457 0.2291752 0.4574226 -2.884 0.003926
## site107 -0.6166431 0.5397533 0.2486007 0.4006009 -1.539 0.123732
## site108 -0.6006326 0.5484645 0.2471343 0.4386309 -1.369 0.170895
## site10R -0.3528529 0.7026806 0.2965086 0.1504501 -2.345 0.019011
## site11R -0.5063852 0.6026701 0.2453713 0.1757924 -2.881 0.003969
## site12R -0.7235460 0.4850293 0.2582603 0.2019557 -3.583 0.000340
## site13R -1.1645825 0.3120529 0.2472749 0.3981161 -2.925 0.003442
## site14R -0.7547489 0.4701287 0.4011653 0.1838000 -4.106 4.02e-05
## site15R -0.2595795 0.7713759 0.2754597 0.0950880 -2.730 0.006336
## site16R -0.4464280 0.6399098 0.2688001 0.1568797 -2.846 0.004432
## site17R -0.8541173 0.4256588 0.2821947 0.3256450 -2.623 0.008720
## site18R -0.8809265 0.4143988 0.2646959 0.3512191 -2.508 0.012135
## site19R -0.0944777 0.9098480 0.2870442 0.0402124 -2.349 0.018800
## site20R -0.8446219 0.4297198 0.2927504 0.2217039 -3.810 0.000139
## site21R -0.7299419 0.4819370 0.2574301 0.1489144 -4.902 9.50e-07
## site22R -0.9623246 0.3820038 0.2552191 0.3391971 -2.837 0.004553
## site23R -0.6833593 0.5049180 0.2559961 0.1952208 -3.500 0.000464
## site24R -0.5870177 0.5559829 0.2552275 0.1526914 -3.844 0.000121
## site25R -0.8361038 0.4333959 0.2597864 0.4574819 -1.828 0.067606
## site26R -0.9617908 0.3822078 0.2712678 0.3435822 -2.799 0.005121
## site27R -0.8947363 0.4087154 0.3739394 0.3489923 -2.564 0.010354
## site28R -1.0948183 0.3346004 0.2749568 0.4349577 -2.517 0.011834
## site29R -0.8943912 0.4088564 0.3397935 0.2852147 -3.136 0.001714
## site30R -1.3838622 0.2506088 0.2647684 0.5178907 -2.672 0.007538
## site31R -0.6411507 0.5266860 0.2755237 0.1871218 -3.426 0.000612
## site32R -0.6397453 0.5274267 0.2782498 0.2005939 -3.189 0.001426
## site33R -0.8386735 0.4322836 0.2640502 0.2631108 -3.188 0.001435
## site34R -0.8935119 0.4092161 0.2544624 0.3242382 -2.756 0.005856
## site35R -0.4066764 0.6658596 0.2703584 0.1046750 -3.885 0.000102
## site36R -1.3746689 0.2529233 0.2643669 0.4480875 -3.068 0.002156
## site37R -1.3791152 0.2518013 0.2661370 0.5086673 -2.711 0.006703
## site38R -0.6795467 0.5068467 0.2671766 0.1557021 -4.364 1.27e-05
## site39R -0.6566515 0.5185849 0.2944146 0.2047666 -3.207 0.001342
## site40R -1.3711046 0.2538264 0.2790531 0.7610127 -1.802 0.071595
## site41R -0.9240745 0.3968986 0.2577492 0.2854500 -3.237 0.001207
## site42R -0.7375335 0.4782922 0.2821641 0.2947501 -2.502 0.012341
##
## NH4 *
## dx_yr .
## age_dx **
## sexF *
## dich_RaceNon-White
## smokeHxFormer *
## smokeHxAlways
## smokeHxUnknown *
## smokeHxEver
## disadv
## site02R
## site03R *
## site04R **
## site05R ***
## site06R ***
## site07R **
## site09R **
## site1 **
## site101 *
## site102 **
## site103 .
## site104 ***
## site105 *
## site106 **
## site107
## site108
## site10R *
## site11R **
## site12R ***
## site13R **
## site14R ***
## site15R **
## site16R **
## site17R **
## site18R *
## site19R *
## site20R ***
## site21R ***
## site22R **
## site23R ***
## site24R ***
## site25R .
## site26R **
## site27R *
## site28R *
## site29R **
## site30R **
## site31R ***
## site32R **
## site33R **
## site34R **
## site35R ***
## site36R **
## site37R **
## site38R ***
## site39R **
## site40R .
## site41R **
## site42R *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NH4 5.5273 0.1809 1.20576 25.3371
## dx_yr 1.4182 0.7051 0.98954 2.0325
## age_dx 1.0098 0.9903 1.00254 1.0170
## sexF 0.8714 1.1476 0.77581 0.9787
## dich_RaceNon-White 1.0107 0.9894 0.95772 1.0667
## smokeHxFormer 1.0958 0.9126 1.01403 1.1842
## smokeHxAlways 0.9998 1.0002 0.92526 1.0804
## smokeHxUnknown 0.9493 1.0534 0.90253 0.9985
## smokeHxEver 0.9905 1.0096 0.96243 1.0194
## disadv 1.0153 0.9849 0.90779 1.1356
## site02R 1.0061 0.9939 0.55680 1.8181
## site03R 0.6873 1.4549 0.49913 0.9465
## site04R 0.3321 3.0115 0.14922 0.7390
## site05R 0.2944 3.3970 0.14549 0.5956
## site06R 0.5847 1.7102 0.47565 0.7188
## site07R 0.2471 4.0468 0.09050 0.6747
## site09R 0.5369 1.8627 0.35667 0.8081
## site1 0.2697 3.7084 0.10792 0.6738
## site101 0.3463 2.8876 0.14656 0.8183
## site102 0.5239 1.9089 0.35290 0.7776
## site103 0.8040 1.2437 0.63208 1.0227
## site104 0.6397 1.5632 0.49255 0.8308
## site105 0.3971 2.5183 0.19294 0.8173
## site106 0.2673 3.7405 0.10907 0.6553
## site107 0.5398 1.8527 0.24615 1.1836
## site108 0.5485 1.8233 0.23216 1.2957
## site10R 0.7027 1.4231 0.52323 0.9437
## site11R 0.6027 1.6593 0.42702 0.8506
## site12R 0.4850 2.0617 0.32648 0.7206
## site13R 0.3121 3.2046 0.14301 0.6809
## site14R 0.4701 2.1271 0.32792 0.6740
## site15R 0.7714 1.2964 0.64022 0.9294
## site16R 0.6399 1.5627 0.47052 0.8703
## site17R 0.4257 2.3493 0.22484 0.8058
## site18R 0.4144 2.4131 0.20819 0.8249
## site19R 0.9098 1.0991 0.84089 0.9845
## site20R 0.4297 2.3271 0.27827 0.6636
## site21R 0.4819 2.0750 0.35994 0.6453
## site22R 0.3820 2.6178 0.19649 0.7427
## site23R 0.5049 1.9805 0.34439 0.7403
## site24R 0.5560 1.7986 0.41218 0.7500
## site25R 0.4334 2.3074 0.17680 1.0624
## site26R 0.3822 2.6164 0.19491 0.7495
## site27R 0.4087 2.4467 0.20623 0.8100
## site28R 0.3346 2.9886 0.14266 0.7848
## site29R 0.4089 2.4458 0.23377 0.7151
## site30R 0.2506 3.9903 0.09082 0.6916
## site31R 0.5267 1.8987 0.36498 0.7600
## site32R 0.5274 1.8960 0.35597 0.7815
## site33R 0.4323 2.3133 0.25811 0.7240
## site34R 0.4092 2.4437 0.21675 0.7726
## site35R 0.6659 1.5018 0.54235 0.8175
## site36R 0.2529 3.9538 0.10509 0.6087
## site37R 0.2518 3.9714 0.09291 0.6824
## site38R 0.5068 1.9730 0.37354 0.6877
## site39R 0.5186 1.9283 0.34715 0.7747
## site40R 0.2538 3.9397 0.05712 1.1280
## site41R 0.3969 2.5195 0.22683 0.6945
## site42R 0.4783 2.0908 0.26841 0.8523
##
## Concordance= 0.68 (se = 0.064 )
## Likelihood ratio test= 2967 on 59 df, p=<2e-16
## Wald test = 8.84 on 59 df, p=1
## Score (logrank) test = 2343 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
summary(All$NH4)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.2151 0.5016 0.6227 0.8336 2.6082 688
IQR(All$NH4, na.rm=T)
## [1] 0.6185003
# Will use the 5yr pre-censoring IQR (0.3400484), not this one
All <- All %>% mutate(NH4_IQR = NH4/0.3400484)
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ NH4_IQR + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NH4_IQR + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All,
## id = ID, cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## NH4_IQR 0.5813776 1.7885006 0.0270153 0.2641634 2.201 0.027748
## dx_yr 0.3493734 1.4181786 0.0087555 0.1836184 1.903 0.057078
## age_dx 0.0097076 1.0097549 0.0011748 0.0036607 2.652 0.008006
## sexF -0.1376702 0.8713860 0.0266937 0.0592741 -2.323 0.020201
## dich_RaceNon-White 0.0106919 1.0107492 0.0367194 0.0274967 0.389 0.697393
## smokeHxFormer 0.0914987 1.0958154 0.0328697 0.0395747 2.312 0.020775
## smokeHxAlways -0.0001908 0.9998093 0.0748577 0.0395371 -0.005 0.996150
## smokeHxUnknown -0.0520493 0.9492820 0.0706821 0.0257670 -2.020 0.043383
## smokeHxEver -0.0095266 0.9905187 0.0499679 0.0146788 -0.649 0.516336
## disadv 0.0151891 1.0153050 0.0453374 0.0571111 0.266 0.790273
## site02R 0.0061263 1.0061451 0.2593874 0.3018780 0.020 0.983809
## site03R -0.3749202 0.6873441 0.2677471 0.1632482 -2.297 0.021640
## site04R -1.1024364 0.3320611 0.2785575 0.4081305 -2.701 0.006909
## site05R -1.2228944 0.2943769 0.2856961 0.3595665 -3.401 0.000671
## site06R -0.5365883 0.5847398 0.2740228 0.1053477 -5.094 3.52e-07
## site07R -1.3979246 0.2471093 0.2537142 0.5124788 -2.728 0.006376
## site09R -0.6220183 0.5368598 0.2765829 0.2086381 -2.981 0.002870
## site1 -1.3106019 0.2696577 0.2295537 0.4672318 -2.805 0.005031
## site101 -1.0604423 0.3463026 0.2291769 0.4387317 -2.417 0.015646
## site102 -0.6465331 0.5238588 0.2273540 0.2015462 -3.208 0.001337
## site103 -0.2181307 0.8040203 0.2227175 0.1227607 -1.777 0.075588
## site104 -0.4467383 0.6397113 0.2282395 0.1333780 -3.349 0.000810
## site105 -0.9235732 0.3970976 0.2262424 0.3682674 -2.508 0.012146
## site106 -1.3192127 0.2673457 0.2291752 0.4574226 -2.884 0.003926
## site107 -0.6166431 0.5397533 0.2486007 0.4006009 -1.539 0.123732
## site108 -0.6006326 0.5484645 0.2471343 0.4386309 -1.369 0.170895
## site10R -0.3528529 0.7026806 0.2965086 0.1504501 -2.345 0.019011
## site11R -0.5063852 0.6026701 0.2453713 0.1757924 -2.881 0.003969
## site12R -0.7235460 0.4850293 0.2582603 0.2019557 -3.583 0.000340
## site13R -1.1645825 0.3120529 0.2472749 0.3981161 -2.925 0.003442
## site14R -0.7547489 0.4701287 0.4011653 0.1838000 -4.106 4.02e-05
## site15R -0.2595795 0.7713759 0.2754597 0.0950880 -2.730 0.006336
## site16R -0.4464280 0.6399098 0.2688001 0.1568797 -2.846 0.004432
## site17R -0.8541173 0.4256588 0.2821947 0.3256450 -2.623 0.008720
## site18R -0.8809265 0.4143988 0.2646959 0.3512191 -2.508 0.012135
## site19R -0.0944777 0.9098480 0.2870442 0.0402124 -2.349 0.018800
## site20R -0.8446219 0.4297198 0.2927504 0.2217039 -3.810 0.000139
## site21R -0.7299419 0.4819370 0.2574301 0.1489144 -4.902 9.50e-07
## site22R -0.9623246 0.3820038 0.2552191 0.3391971 -2.837 0.004553
## site23R -0.6833593 0.5049180 0.2559961 0.1952208 -3.500 0.000464
## site24R -0.5870177 0.5559829 0.2552275 0.1526914 -3.844 0.000121
## site25R -0.8361038 0.4333959 0.2597864 0.4574819 -1.828 0.067606
## site26R -0.9617908 0.3822078 0.2712678 0.3435822 -2.799 0.005121
## site27R -0.8947363 0.4087154 0.3739394 0.3489923 -2.564 0.010354
## site28R -1.0948183 0.3346004 0.2749568 0.4349577 -2.517 0.011834
## site29R -0.8943912 0.4088564 0.3397935 0.2852147 -3.136 0.001714
## site30R -1.3838622 0.2506088 0.2647684 0.5178907 -2.672 0.007538
## site31R -0.6411507 0.5266860 0.2755237 0.1871218 -3.426 0.000612
## site32R -0.6397453 0.5274267 0.2782498 0.2005939 -3.189 0.001426
## site33R -0.8386735 0.4322836 0.2640502 0.2631108 -3.188 0.001435
## site34R -0.8935119 0.4092161 0.2544624 0.3242382 -2.756 0.005856
## site35R -0.4066764 0.6658596 0.2703584 0.1046750 -3.885 0.000102
## site36R -1.3746689 0.2529233 0.2643669 0.4480875 -3.068 0.002156
## site37R -1.3791152 0.2518013 0.2661370 0.5086673 -2.711 0.006703
## site38R -0.6795467 0.5068467 0.2671766 0.1557021 -4.364 1.27e-05
## site39R -0.6566515 0.5185849 0.2944146 0.2047666 -3.207 0.001342
## site40R -1.3711046 0.2538264 0.2790531 0.7610127 -1.802 0.071595
## site41R -0.9240745 0.3968986 0.2577492 0.2854500 -3.237 0.001207
## site42R -0.7375335 0.4782922 0.2821641 0.2947501 -2.502 0.012341
##
## NH4_IQR *
## dx_yr .
## age_dx **
## sexF *
## dich_RaceNon-White
## smokeHxFormer *
## smokeHxAlways
## smokeHxUnknown *
## smokeHxEver
## disadv
## site02R
## site03R *
## site04R **
## site05R ***
## site06R ***
## site07R **
## site09R **
## site1 **
## site101 *
## site102 **
## site103 .
## site104 ***
## site105 *
## site106 **
## site107
## site108
## site10R *
## site11R **
## site12R ***
## site13R **
## site14R ***
## site15R **
## site16R **
## site17R **
## site18R *
## site19R *
## site20R ***
## site21R ***
## site22R **
## site23R ***
## site24R ***
## site25R .
## site26R **
## site27R *
## site28R *
## site29R **
## site30R **
## site31R ***
## site32R **
## site33R **
## site34R **
## site35R ***
## site36R **
## site37R **
## site38R ***
## site39R **
## site40R .
## site41R **
## site42R *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NH4_IQR 1.7885 0.5591 1.06569 3.0015
## dx_yr 1.4182 0.7051 0.98954 2.0325
## age_dx 1.0098 0.9903 1.00254 1.0170
## sexF 0.8714 1.1476 0.77581 0.9787
## dich_RaceNon-White 1.0107 0.9894 0.95772 1.0667
## smokeHxFormer 1.0958 0.9126 1.01403 1.1842
## smokeHxAlways 0.9998 1.0002 0.92526 1.0804
## smokeHxUnknown 0.9493 1.0534 0.90253 0.9985
## smokeHxEver 0.9905 1.0096 0.96243 1.0194
## disadv 1.0153 0.9849 0.90779 1.1356
## site02R 1.0061 0.9939 0.55680 1.8181
## site03R 0.6873 1.4549 0.49913 0.9465
## site04R 0.3321 3.0115 0.14922 0.7390
## site05R 0.2944 3.3970 0.14549 0.5956
## site06R 0.5847 1.7102 0.47565 0.7188
## site07R 0.2471 4.0468 0.09050 0.6747
## site09R 0.5369 1.8627 0.35667 0.8081
## site1 0.2697 3.7084 0.10792 0.6738
## site101 0.3463 2.8876 0.14656 0.8183
## site102 0.5239 1.9089 0.35290 0.7776
## site103 0.8040 1.2437 0.63208 1.0227
## site104 0.6397 1.5632 0.49255 0.8308
## site105 0.3971 2.5183 0.19294 0.8173
## site106 0.2673 3.7405 0.10907 0.6553
## site107 0.5398 1.8527 0.24615 1.1836
## site108 0.5485 1.8233 0.23216 1.2957
## site10R 0.7027 1.4231 0.52323 0.9437
## site11R 0.6027 1.6593 0.42702 0.8506
## site12R 0.4850 2.0617 0.32648 0.7206
## site13R 0.3121 3.2046 0.14301 0.6809
## site14R 0.4701 2.1271 0.32792 0.6740
## site15R 0.7714 1.2964 0.64022 0.9294
## site16R 0.6399 1.5627 0.47052 0.8703
## site17R 0.4257 2.3493 0.22484 0.8058
## site18R 0.4144 2.4131 0.20819 0.8249
## site19R 0.9098 1.0991 0.84089 0.9845
## site20R 0.4297 2.3271 0.27827 0.6636
## site21R 0.4819 2.0750 0.35994 0.6453
## site22R 0.3820 2.6178 0.19649 0.7427
## site23R 0.5049 1.9805 0.34439 0.7403
## site24R 0.5560 1.7986 0.41218 0.7500
## site25R 0.4334 2.3074 0.17680 1.0624
## site26R 0.3822 2.6164 0.19491 0.7495
## site27R 0.4087 2.4467 0.20623 0.8100
## site28R 0.3346 2.9886 0.14266 0.7848
## site29R 0.4089 2.4458 0.23377 0.7151
## site30R 0.2506 3.9903 0.09082 0.6916
## site31R 0.5267 1.8987 0.36498 0.7600
## site32R 0.5274 1.8960 0.35597 0.7815
## site33R 0.4323 2.3133 0.25811 0.7240
## site34R 0.4092 2.4437 0.21675 0.7726
## site35R 0.6659 1.5018 0.54235 0.8175
## site36R 0.2529 3.9538 0.10509 0.6087
## site37R 0.2518 3.9714 0.09291 0.6824
## site38R 0.5068 1.9730 0.37354 0.6877
## site39R 0.5186 1.9283 0.34715 0.7747
## site40R 0.2538 3.9397 0.05712 1.1280
## site41R 0.3969 2.5195 0.22683 0.6945
## site42R 0.4783 2.0908 0.26841 0.8523
##
## Concordance= 0.68 (se = 0.064 )
## Likelihood ratio test= 2967 on 59 df, p=<2e-16
## Wald test = 8.84 on 59 df, p=1
## Score (logrank) test = 2343 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
So this indicates that there is a HR of 1.78 per IQR increase in NH4 as compared with a HR of 5.41 per 1ug/m3 increase in NH4.
Base model
#First need to make dataframe that only includes patients with a value for event
Allx <- All %>% filter(!is.na(NH4) & !is.na(deadORtx) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(cohort) & !is.na(site) & NH4>0.005 & NH4<2.55)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$NH4, df=3) + Allx$dx_yr + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$NH4, exp(predicted$fit), type="n")
lines(sm.spline(Allx$NH4, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$NH4, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$NH4, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
Complete model
#First need to make dataframe that only includes patients with time_DeathTxCensor
Allx <- All %>% filter(!is.na(NH4) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(deadORtx) & !is.na(age_dx) & !is.na(sex) & !is.na(smokeHx) & !is.na(dich_Race) & !is.na(disadv) & !is.na(site) & NH4>0.005 & NH4<2.55)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$NH4, df=3) + Allx$dx_yr + Allx$age_dx + Allx$sex + Allx$smokeHx + Allx$dich_Race + Allx$disadv + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$NH4, exp(predicted$fit), type="n")
lines(sm.spline(Allx$NH4, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$NH4, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$NH4, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ BC + dx_yr + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ BC + dx_yr + site,
## data = All, id = ID, cluster = cohort)
##
## n= 335367, number of events= 6459
## (1981 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## BC 0.623396 1.865252 0.075663 0.135620 4.597 4.29e-06 ***
## dx_yr 0.224451 1.251636 0.005420 0.147413 1.523 0.127858
## site02R 0.431520 1.539595 0.253982 0.135957 3.174 0.001504 **
## site03R -0.263787 0.768137 0.263289 0.054179 -4.869 1.12e-06 ***
## site04R -0.313005 0.731247 0.271016 0.049687 -6.300 2.98e-10 ***
## site05R -0.375044 0.687259 0.276191 0.064815 -5.786 7.19e-09 ***
## site06R -0.043820 0.957127 0.267174 0.143003 -0.306 0.759281
## site07R -0.498761 0.607283 0.245524 0.162795 -3.064 0.002186 **
## site09R -0.122208 0.884965 0.269307 0.048677 -2.511 0.012052 *
## site1 -0.318778 0.727037 0.216915 0.053073 -6.006 1.90e-09 ***
## site101 -0.392850 0.675130 0.220180 0.230311 -1.706 0.088057 .
## site102 -0.405460 0.666670 0.220117 0.205969 -1.969 0.049005 *
## site103 -0.147002 0.863292 0.215501 0.150626 -0.976 0.329092
## site104 -0.344090 0.708865 0.221272 0.159980 -2.151 0.031490 *
## site105 -0.327090 0.721019 0.217362 0.227804 -1.436 0.151048
## site106 -0.447346 0.639323 0.218863 0.172019 -2.601 0.009307 **
## site107 0.003107 1.003112 0.240679 0.270037 0.012 0.990820
## site108 -0.060045 0.941722 0.240359 0.420577 -0.143 0.886473
## site10R -0.183892 0.832025 0.289868 0.156225 -1.177 0.239155
## site11R -0.273312 0.760855 0.239523 0.048824 -5.598 2.17e-08 ***
## site12R -0.090473 0.913499 0.252312 0.005599 -16.158 < 2e-16 ***
## site13R -0.500289 0.606355 0.240878 0.150536 -3.323 0.000889 ***
## site14R -0.016089 0.984040 0.396074 0.212863 -0.076 0.939751
## site15R -0.094109 0.910184 0.270979 0.027214 -3.458 0.000544 ***
## site16R 0.025005 1.025320 0.263577 0.099605 0.251 0.801782
## site17R -0.174267 0.840073 0.275521 0.022825 -7.635 2.26e-14 ***
## site18R -0.519501 0.594817 0.259575 0.253124 -2.052 0.040135 *
## site19R -0.020643 0.979569 0.282872 0.076743 -0.269 0.787941
## site20R -0.266995 0.765677 0.287275 0.015747 -16.955 < 2e-16 ***
## site21R -0.518074 0.595667 0.253815 0.049981 -10.365 < 2e-16 ***
## site22R -0.201852 0.817216 0.247915 0.027099 -7.449 9.43e-14 ***
## site23R -0.212928 0.808214 0.248258 0.018201 -11.699 < 2e-16 ***
## site24R 0.020601 1.020814 0.248752 0.094546 0.218 0.827516
## site25R -0.193998 0.823660 0.250639 0.087121 -2.227 0.025962 *
## site26R -0.371342 0.689808 0.265489 0.119644 -3.104 0.001911 **
## site27R -0.245104 0.782623 0.369424 0.048899 -5.012 5.37e-07 ***
## site28R -0.242614 0.784575 0.266395 0.031409 -7.724 1.12e-14 ***
## site29R -0.372649 0.688907 0.334905 0.099371 -3.750 0.000177 ***
## site30R -0.455788 0.633948 0.255960 0.132196 -3.448 0.000565 ***
## site31R -0.259066 0.771772 0.270718 0.033555 -7.721 1.16e-14 ***
## site32R -0.494954 0.609599 0.268103 0.110189 -4.492 7.06e-06 ***
## site33R -0.313828 0.730645 0.257513 0.072641 -4.320 1.56e-05 ***
## site34R -0.237651 0.788478 0.247689 0.091933 -2.585 0.009736 **
## site35R -0.075334 0.927433 0.264290 0.022454 -3.355 0.000793 ***
## site36R -0.404516 0.667299 0.255283 0.036115 -11.201 < 2e-16 ***
## site37R -0.391680 0.675921 0.257259 0.106632 -3.673 0.000240 ***
## site38R -0.369925 0.690786 0.261322 0.034814 -10.626 < 2e-16 ***
## site39R -0.338845 0.712593 0.289895 0.063769 -5.314 1.07e-07 ***
## site40R -0.508129 0.601620 0.274668 0.125490 -4.049 5.14e-05 ***
## site41R -0.422185 0.655613 0.252358 0.058167 -7.258 3.92e-13 ***
## site42R -0.007998 0.992034 0.275752 0.054090 -0.148 0.882454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BC 1.8653 0.5361 1.4299 2.4332
## dx_yr 1.2516 0.7990 0.9376 1.6709
## site02R 1.5396 0.6495 1.1794 2.0097
## site03R 0.7681 1.3019 0.6908 0.8542
## site04R 0.7312 1.3675 0.6634 0.8060
## site05R 0.6873 1.4551 0.6053 0.7804
## site06R 0.9571 1.0448 0.7232 1.2668
## site07R 0.6073 1.6467 0.4414 0.8355
## site09R 0.8850 1.1300 0.8044 0.9736
## site1 0.7270 1.3754 0.6552 0.8067
## site101 0.6751 1.4812 0.4299 1.0603
## site102 0.6667 1.5000 0.4452 0.9982
## site103 0.8633 1.1584 0.6426 1.1598
## site104 0.7089 1.4107 0.5181 0.9699
## site105 0.7210 1.3869 0.4614 1.1268
## site106 0.6393 1.5642 0.4563 0.8957
## site107 1.0031 0.9969 0.5909 1.7030
## site108 0.9417 1.0619 0.4130 2.1474
## site10R 0.8320 1.2019 0.6126 1.1301
## site11R 0.7609 1.3143 0.6914 0.8373
## site12R 0.9135 1.0947 0.9035 0.9236
## site13R 0.6064 1.6492 0.4514 0.8144
## site14R 0.9840 1.0162 0.6484 1.4935
## site15R 0.9102 1.0987 0.8629 0.9600
## site16R 1.0253 0.9753 0.8435 1.2464
## site17R 0.8401 1.1904 0.8033 0.8785
## site18R 0.5948 1.6812 0.3622 0.9769
## site19R 0.9796 1.0209 0.8428 1.1386
## site20R 0.7657 1.3060 0.7424 0.7897
## site21R 0.5957 1.6788 0.5401 0.6570
## site22R 0.8172 1.2237 0.7749 0.8618
## site23R 0.8082 1.2373 0.7799 0.8376
## site24R 1.0208 0.9796 0.8481 1.2286
## site25R 0.8237 1.2141 0.6944 0.9770
## site26R 0.6898 1.4497 0.5456 0.8721
## site27R 0.7826 1.2778 0.7111 0.8613
## site28R 0.7846 1.2746 0.7377 0.8344
## site29R 0.6889 1.4516 0.5670 0.8370
## site30R 0.6339 1.5774 0.4892 0.8214
## site31R 0.7718 1.2957 0.7226 0.8242
## site32R 0.6096 1.6404 0.4912 0.7565
## site33R 0.7306 1.3687 0.6337 0.8424
## site34R 0.7885 1.2683 0.6585 0.9442
## site35R 0.9274 1.0782 0.8875 0.9692
## site36R 0.6673 1.4986 0.6217 0.7162
## site37R 0.6759 1.4795 0.5484 0.8330
## site38R 0.6908 1.4476 0.6452 0.7396
## site39R 0.7126 1.4033 0.6289 0.8075
## site40R 0.6016 1.6622 0.4704 0.7694
## site41R 0.6556 1.5253 0.5850 0.7348
## site42R 0.9920 1.0080 0.8922 1.1030
##
## Concordance= 0.67 (se = 0.065 )
## Likelihood ratio test= 2435 on 51 df, p=<2e-16
## Wald test = 99.77 on 51 df, p=5e-05
## Score (logrank) test = 1986 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ BC + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ BC + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = All, id = ID,
## cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## BC 0.673552 1.961191 0.077570 0.117165 5.749 8.99e-09 ***
## dx_yr 0.221454 1.247889 0.005575 0.150231 1.474 0.140459
## age_dx 0.009712 1.009760 0.001176 0.003881 2.503 0.012324 *
## sexF -0.139078 0.870160 0.026697 0.059383 -2.342 0.019178 *
## dich_RaceNon-White -0.021876 0.978361 0.036935 0.025429 -0.860 0.389625
## smokeHxFormer 0.121988 1.129740 0.032976 0.049859 2.447 0.014420 *
## smokeHxAlways 0.050602 1.051904 0.074836 0.035797 1.414 0.157478
## smokeHxUnknown 0.052147 1.053530 0.070625 0.037597 1.387 0.165448
## smokeHxEver -0.009495 0.990550 0.049954 0.013453 -0.706 0.480295
## disadv -0.040944 0.959883 0.045721 0.060060 -0.682 0.495413
## site02R 0.386991 1.472543 0.258511 0.127605 3.033 0.002424 **
## site03R -0.277760 0.757479 0.267717 0.046802 -5.935 2.94e-09 ***
## site04R -0.303759 0.738039 0.275473 0.049839 -6.095 1.10e-09 ***
## site05R -0.379243 0.684379 0.282509 0.084946 -4.465 8.03e-06 ***
## site06R -0.101138 0.903808 0.273196 0.181960 -0.556 0.578330
## site07R -0.565772 0.567922 0.250240 0.147157 -3.845 0.000121 ***
## site09R -0.195678 0.822277 0.275732 0.031338 -6.244 4.26e-10 ***
## site1 -0.426565 0.652748 0.224825 0.029250 -14.583 < 2e-16 ***
## site101 -0.440153 0.643938 0.227016 0.202540 -2.173 0.029767 *
## site102 -0.481747 0.617704 0.227139 0.181988 -2.647 0.008118 **
## site103 -0.200948 0.817955 0.222570 0.113322 -1.773 0.076186 .
## site104 -0.425795 0.653250 0.228170 0.117690 -3.618 0.000297 ***
## site105 -0.412471 0.662012 0.224760 0.193920 -2.127 0.033418 *
## site106 -0.589617 0.554540 0.226189 0.126919 -4.646 3.39e-06 ***
## site107 -0.083000 0.920351 0.247252 0.235845 -0.352 0.724893
## site108 -0.152065 0.858932 0.246493 0.381234 -0.399 0.689984
## site10R -0.245273 0.782491 0.296604 0.137496 -1.784 0.074447 .
## site11R -0.367948 0.692153 0.246178 0.035616 -10.331 < 2e-16 ***
## site12R -0.148223 0.862239 0.256887 0.021274 -6.967 3.23e-12 ***
## site13R -0.613442 0.541484 0.245795 0.139230 -4.406 1.05e-05 ***
## site14R -0.039754 0.961026 0.399130 0.215195 -0.185 0.853438
## site15R -0.178205 0.836771 0.275710 0.030542 -5.835 5.39e-09 ***
## site16R 0.015635 1.015758 0.267909 0.100250 0.156 0.876065
## site17R -0.160072 0.852082 0.279938 0.016169 -9.900 < 2e-16 ***
## site18R -0.576027 0.562127 0.264269 0.227828 -2.528 0.011460 *
## site19R -0.082257 0.921035 0.287101 0.052684 -1.561 0.118448
## site20R -0.312229 0.731814 0.291564 0.015882 -19.660 < 2e-16 ***
## site21R -0.590003 0.554326 0.258582 0.062101 -9.501 < 2e-16 ***
## site22R -0.291291 0.747298 0.252947 0.028063 -10.380 < 2e-16 ***
## site23R -0.256284 0.773922 0.255155 0.016194 -15.826 < 2e-16 ***
## site24R 0.022769 1.023030 0.253591 0.102021 0.223 0.823396
## site25R -0.245571 0.782258 0.256956 0.084704 -2.899 0.003742 **
## site26R -0.432789 0.648697 0.270064 0.102716 -4.213 2.52e-05 ***
## site27R -0.258762 0.772007 0.372569 0.041097 -6.296 3.05e-10 ***
## site28R -0.193701 0.823904 0.271076 0.011085 -17.474 < 2e-16 ***
## site29R -0.444298 0.641274 0.339096 0.102215 -4.347 1.38e-05 ***
## site30R -0.536771 0.584633 0.261318 0.120715 -4.447 8.72e-06 ***
## site31R -0.283166 0.753394 0.274947 0.024938 -11.355 < 2e-16 ***
## site32R -0.511520 0.599584 0.278187 0.082449 -6.204 5.50e-10 ***
## site33R -0.366015 0.693492 0.263026 0.045881 -7.978 1.49e-15 ***
## site34R -0.304850 0.737234 0.252880 0.080664 -3.779 0.000157 ***
## site35R -0.152172 0.858840 0.270176 0.014040 -10.839 < 2e-16 ***
## site36R -0.434888 0.647337 0.260064 0.035142 -12.375 < 2e-16 ***
## site37R -0.466666 0.627090 0.261932 0.097650 -4.779 1.76e-06 ***
## site38R -0.416159 0.659575 0.266938 0.024377 -17.072 < 2e-16 ***
## site39R -0.422743 0.655247 0.294275 0.041289 -10.239 < 2e-16 ***
## site40R -0.621647 0.537059 0.281199 0.107376 -5.789 7.06e-09 ***
## site41R -0.462487 0.629716 0.256939 0.047685 -9.699 < 2e-16 ***
## site42R -0.040619 0.960195 0.280009 0.055601 -0.731 0.465055
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BC 1.9612 0.5099 1.5588 2.4675
## dx_yr 1.2479 0.8014 0.9296 1.6751
## age_dx 1.0098 0.9903 1.0021 1.0175
## sexF 0.8702 1.1492 0.7746 0.9776
## dich_RaceNon-White 0.9784 1.0221 0.9308 1.0284
## smokeHxFormer 1.1297 0.8852 1.0246 1.2457
## smokeHxAlways 1.0519 0.9507 0.9806 1.1284
## smokeHxUnknown 1.0535 0.9492 0.9787 1.1341
## smokeHxEver 0.9905 1.0095 0.9648 1.0170
## disadv 0.9599 1.0418 0.8533 1.0798
## site02R 1.4725 0.6791 1.1467 1.8910
## site03R 0.7575 1.3202 0.6911 0.8302
## site04R 0.7380 1.3549 0.6694 0.8138
## site05R 0.6844 1.4612 0.5794 0.8084
## site06R 0.9038 1.1064 0.6327 1.2911
## site07R 0.5679 1.7608 0.4256 0.7578
## site09R 0.8223 1.2161 0.7733 0.8744
## site1 0.6527 1.5320 0.6164 0.6913
## site101 0.6439 1.5529 0.4330 0.9577
## site102 0.6177 1.6189 0.4324 0.8824
## site103 0.8180 1.2226 0.6550 1.0214
## site104 0.6532 1.5308 0.5187 0.8227
## site105 0.6620 1.5105 0.4527 0.9681
## site106 0.5545 1.8033 0.4324 0.7112
## site107 0.9204 1.0865 0.5797 1.4612
## site108 0.8589 1.1642 0.4069 1.8133
## site10R 0.7825 1.2780 0.5976 1.0245
## site11R 0.6922 1.4448 0.6455 0.7422
## site12R 0.8622 1.1598 0.8270 0.8990
## site13R 0.5415 1.8468 0.4122 0.7114
## site14R 0.9610 1.0406 0.6303 1.4652
## site15R 0.8368 1.1951 0.7882 0.8884
## site16R 1.0158 0.9845 0.8346 1.2363
## site17R 0.8521 1.1736 0.8255 0.8795
## site18R 0.5621 1.7790 0.3597 0.8785
## site19R 0.9210 1.0857 0.8307 1.0212
## site20R 0.7318 1.3665 0.7094 0.7550
## site21R 0.5543 1.8040 0.4908 0.6261
## site22R 0.7473 1.3382 0.7073 0.7896
## site23R 0.7739 1.2921 0.7497 0.7989
## site24R 1.0230 0.9775 0.8376 1.2495
## site25R 0.7823 1.2784 0.6626 0.9235
## site26R 0.6487 1.5416 0.5304 0.7934
## site27R 0.7720 1.2953 0.7123 0.8368
## site28R 0.8239 1.2137 0.8062 0.8420
## site29R 0.6413 1.5594 0.5249 0.7835
## site30R 0.5846 1.7105 0.4615 0.7407
## site31R 0.7534 1.3273 0.7175 0.7911
## site32R 0.5996 1.6678 0.5101 0.7047
## site33R 0.6935 1.4420 0.6339 0.7587
## site34R 0.7372 1.3564 0.6294 0.8635
## site35R 0.8588 1.1644 0.8355 0.8828
## site36R 0.6473 1.5448 0.6043 0.6935
## site37R 0.6271 1.5947 0.5179 0.7594
## site38R 0.6596 1.5161 0.6288 0.6919
## site39R 0.6552 1.5261 0.6043 0.7105
## site40R 0.5371 1.8620 0.4351 0.6629
## site41R 0.6297 1.5880 0.5735 0.6914
## site42R 0.9602 1.0415 0.8611 1.0707
##
## Concordance= 0.674 (se = 0.063 )
## Likelihood ratio test= 2610 on 59 df, p=<2e-16
## Wald test = 178 on 59 df, p=7e-14
## Score (logrank) test = 2154 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
summary(All$BC)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0401 0.4526 0.6276 0.6228 0.7736 2.5448 688
IQR(All$BC, na.rm=T)
## [1] 0.3210303
# Will use the 5yr pre-censoring IQR (0.325731), not this one
All <- All %>% mutate(BC_IQR = BC/0.325731)
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ BC_IQR + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ BC_IQR + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All,
## id = ID, cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## BC_IQR 0.219397 1.245325 0.025267 0.038164 5.749 8.99e-09 ***
## dx_yr 0.221454 1.247889 0.005575 0.150231 1.474 0.140459
## age_dx 0.009712 1.009760 0.001176 0.003881 2.503 0.012324 *
## sexF -0.139078 0.870160 0.026697 0.059383 -2.342 0.019178 *
## dich_RaceNon-White -0.021876 0.978361 0.036935 0.025429 -0.860 0.389625
## smokeHxFormer 0.121988 1.129740 0.032976 0.049859 2.447 0.014420 *
## smokeHxAlways 0.050602 1.051904 0.074836 0.035797 1.414 0.157478
## smokeHxUnknown 0.052147 1.053530 0.070625 0.037597 1.387 0.165448
## smokeHxEver -0.009495 0.990550 0.049954 0.013453 -0.706 0.480295
## disadv -0.040944 0.959883 0.045721 0.060060 -0.682 0.495413
## site02R 0.386991 1.472543 0.258511 0.127605 3.033 0.002424 **
## site03R -0.277760 0.757479 0.267717 0.046802 -5.935 2.94e-09 ***
## site04R -0.303759 0.738039 0.275473 0.049839 -6.095 1.10e-09 ***
## site05R -0.379243 0.684379 0.282509 0.084946 -4.465 8.03e-06 ***
## site06R -0.101138 0.903808 0.273196 0.181960 -0.556 0.578330
## site07R -0.565772 0.567922 0.250240 0.147157 -3.845 0.000121 ***
## site09R -0.195678 0.822277 0.275732 0.031338 -6.244 4.26e-10 ***
## site1 -0.426565 0.652748 0.224825 0.029250 -14.583 < 2e-16 ***
## site101 -0.440153 0.643938 0.227016 0.202540 -2.173 0.029767 *
## site102 -0.481747 0.617704 0.227139 0.181988 -2.647 0.008118 **
## site103 -0.200948 0.817955 0.222570 0.113322 -1.773 0.076186 .
## site104 -0.425795 0.653250 0.228170 0.117690 -3.618 0.000297 ***
## site105 -0.412471 0.662012 0.224760 0.193920 -2.127 0.033418 *
## site106 -0.589617 0.554540 0.226189 0.126919 -4.646 3.39e-06 ***
## site107 -0.083000 0.920351 0.247252 0.235845 -0.352 0.724893
## site108 -0.152065 0.858932 0.246493 0.381234 -0.399 0.689984
## site10R -0.245273 0.782491 0.296604 0.137496 -1.784 0.074447 .
## site11R -0.367948 0.692153 0.246178 0.035616 -10.331 < 2e-16 ***
## site12R -0.148223 0.862239 0.256887 0.021274 -6.967 3.23e-12 ***
## site13R -0.613442 0.541484 0.245795 0.139230 -4.406 1.05e-05 ***
## site14R -0.039754 0.961026 0.399130 0.215195 -0.185 0.853438
## site15R -0.178205 0.836771 0.275710 0.030542 -5.835 5.39e-09 ***
## site16R 0.015635 1.015758 0.267909 0.100250 0.156 0.876065
## site17R -0.160072 0.852082 0.279938 0.016169 -9.900 < 2e-16 ***
## site18R -0.576027 0.562127 0.264269 0.227828 -2.528 0.011460 *
## site19R -0.082257 0.921035 0.287101 0.052684 -1.561 0.118448
## site20R -0.312229 0.731814 0.291564 0.015882 -19.660 < 2e-16 ***
## site21R -0.590003 0.554326 0.258582 0.062101 -9.501 < 2e-16 ***
## site22R -0.291291 0.747298 0.252947 0.028063 -10.380 < 2e-16 ***
## site23R -0.256284 0.773922 0.255155 0.016194 -15.826 < 2e-16 ***
## site24R 0.022769 1.023030 0.253591 0.102021 0.223 0.823396
## site25R -0.245571 0.782258 0.256956 0.084704 -2.899 0.003742 **
## site26R -0.432789 0.648697 0.270064 0.102716 -4.213 2.52e-05 ***
## site27R -0.258762 0.772007 0.372569 0.041097 -6.296 3.05e-10 ***
## site28R -0.193701 0.823904 0.271076 0.011085 -17.474 < 2e-16 ***
## site29R -0.444298 0.641274 0.339096 0.102215 -4.347 1.38e-05 ***
## site30R -0.536771 0.584633 0.261318 0.120715 -4.447 8.72e-06 ***
## site31R -0.283166 0.753394 0.274947 0.024938 -11.355 < 2e-16 ***
## site32R -0.511520 0.599584 0.278187 0.082449 -6.204 5.50e-10 ***
## site33R -0.366015 0.693492 0.263026 0.045881 -7.978 1.49e-15 ***
## site34R -0.304850 0.737234 0.252880 0.080664 -3.779 0.000157 ***
## site35R -0.152172 0.858840 0.270176 0.014040 -10.839 < 2e-16 ***
## site36R -0.434888 0.647337 0.260064 0.035142 -12.375 < 2e-16 ***
## site37R -0.466666 0.627090 0.261932 0.097650 -4.779 1.76e-06 ***
## site38R -0.416159 0.659575 0.266938 0.024377 -17.072 < 2e-16 ***
## site39R -0.422743 0.655247 0.294275 0.041289 -10.239 < 2e-16 ***
## site40R -0.621647 0.537059 0.281199 0.107376 -5.789 7.06e-09 ***
## site41R -0.462487 0.629716 0.256939 0.047685 -9.699 < 2e-16 ***
## site42R -0.040619 0.960195 0.280009 0.055601 -0.731 0.465055
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BC_IQR 1.2453 0.8030 1.1556 1.3420
## dx_yr 1.2479 0.8014 0.9296 1.6751
## age_dx 1.0098 0.9903 1.0021 1.0175
## sexF 0.8702 1.1492 0.7746 0.9776
## dich_RaceNon-White 0.9784 1.0221 0.9308 1.0284
## smokeHxFormer 1.1297 0.8852 1.0246 1.2457
## smokeHxAlways 1.0519 0.9507 0.9806 1.1284
## smokeHxUnknown 1.0535 0.9492 0.9787 1.1341
## smokeHxEver 0.9905 1.0095 0.9648 1.0170
## disadv 0.9599 1.0418 0.8533 1.0798
## site02R 1.4725 0.6791 1.1467 1.8910
## site03R 0.7575 1.3202 0.6911 0.8302
## site04R 0.7380 1.3549 0.6694 0.8138
## site05R 0.6844 1.4612 0.5794 0.8084
## site06R 0.9038 1.1064 0.6327 1.2911
## site07R 0.5679 1.7608 0.4256 0.7578
## site09R 0.8223 1.2161 0.7733 0.8744
## site1 0.6527 1.5320 0.6164 0.6913
## site101 0.6439 1.5529 0.4330 0.9577
## site102 0.6177 1.6189 0.4324 0.8824
## site103 0.8180 1.2226 0.6550 1.0214
## site104 0.6532 1.5308 0.5187 0.8227
## site105 0.6620 1.5105 0.4527 0.9681
## site106 0.5545 1.8033 0.4324 0.7112
## site107 0.9204 1.0865 0.5797 1.4612
## site108 0.8589 1.1642 0.4069 1.8133
## site10R 0.7825 1.2780 0.5976 1.0245
## site11R 0.6922 1.4448 0.6455 0.7422
## site12R 0.8622 1.1598 0.8270 0.8990
## site13R 0.5415 1.8468 0.4122 0.7114
## site14R 0.9610 1.0406 0.6303 1.4652
## site15R 0.8368 1.1951 0.7882 0.8884
## site16R 1.0158 0.9845 0.8346 1.2363
## site17R 0.8521 1.1736 0.8255 0.8795
## site18R 0.5621 1.7790 0.3597 0.8785
## site19R 0.9210 1.0857 0.8307 1.0212
## site20R 0.7318 1.3665 0.7094 0.7550
## site21R 0.5543 1.8040 0.4908 0.6261
## site22R 0.7473 1.3382 0.7073 0.7896
## site23R 0.7739 1.2921 0.7497 0.7989
## site24R 1.0230 0.9775 0.8376 1.2495
## site25R 0.7823 1.2784 0.6626 0.9235
## site26R 0.6487 1.5416 0.5304 0.7934
## site27R 0.7720 1.2953 0.7123 0.8368
## site28R 0.8239 1.2137 0.8062 0.8420
## site29R 0.6413 1.5594 0.5249 0.7835
## site30R 0.5846 1.7105 0.4615 0.7407
## site31R 0.7534 1.3273 0.7175 0.7911
## site32R 0.5996 1.6678 0.5101 0.7047
## site33R 0.6935 1.4420 0.6339 0.7587
## site34R 0.7372 1.3564 0.6294 0.8635
## site35R 0.8588 1.1644 0.8355 0.8828
## site36R 0.6473 1.5448 0.6043 0.6935
## site37R 0.6271 1.5947 0.5179 0.7594
## site38R 0.6596 1.5161 0.6288 0.6919
## site39R 0.6552 1.5261 0.6043 0.7105
## site40R 0.5371 1.8620 0.4351 0.6629
## site41R 0.6297 1.5880 0.5735 0.6914
## site42R 0.9602 1.0415 0.8611 1.0707
##
## Concordance= 0.674 (se = 0.063 )
## Likelihood ratio test= 2610 on 59 df, p=<2e-16
## Wald test = 178 on 59 df, p=7e-14
## Score (logrank) test = 2154 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
So this indicates that there is a HR of 1.24 per IQR increase in BC as compared with a HR of 1.96 per 1ug/m3 increase in BC.
Base model
#First need to make dataframe that only includes patients with a value for event
Allx <- All %>% filter(!is.na(BC) & !is.na(deadORtx) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(cohort) & !is.na(site) & BC<20)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$BC, df=3) + Allx$dx_yr + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$BC, exp(predicted$fit), type="n")
lines(sm.spline(Allx$BC, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$BC, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$BC, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
Complete model
#First need to make dataframe that only includes patients with time_DeathTxCensor
Allx <- All %>% filter(!is.na(BC) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(deadORtx) & !is.na(age_dx) & !is.na(sex) & !is.na(smokeHx) & !is.na(dich_Race) & !is.na(disadv) & !is.na(site) & BC<20)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$BC, df=3) + Allx$dx_yr + Allx$age_dx + Allx$sex + Allx$smokeHx + Allx$dich_Race + Allx$disadv + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$BC, exp(predicted$fit), type="n")
lines(sm.spline(Allx$BC, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$BC, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$BC, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ OM + dx_yr + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ OM + dx_yr + site,
## data = All, id = ID, cluster = cohort)
##
## n= 335367, number of events= 6459
## (1981 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## OM -0.018766 0.981409 0.012899 0.035398 -0.530 0.596018
## dx_yr 0.218261 1.243912 0.005452 0.151847 1.437 0.150613
## site02R 0.483316 1.621443 0.254007 0.140111 3.450 0.000562 ***
## site03R -0.218358 0.803837 0.263214 0.099085 -2.204 0.027542 *
## site04R -0.189271 0.827563 0.270642 0.087201 -2.171 0.029968 *
## site05R -0.173216 0.840956 0.275463 0.038227 -4.531 5.86e-06 ***
## site06R 0.104855 1.110549 0.266760 0.118316 0.886 0.375497
## site07R -0.415717 0.659867 0.245391 0.184951 -2.248 0.024595 *
## site09R -0.072365 0.930191 0.269252 0.090329 -0.801 0.423056
## site1 -0.109553 0.896235 0.215607 0.088138 -1.243 0.213882
## site101 -0.371492 0.689705 0.220191 0.250361 -1.484 0.137855
## site102 -0.451100 0.636927 0.220048 0.212434 -2.123 0.033713 *
## site103 -0.161757 0.850648 0.215502 0.152965 -1.057 0.290293
## site104 -0.333394 0.716488 0.221333 0.157415 -2.118 0.034181 *
## site105 -0.308440 0.734592 0.217572 0.231860 -1.330 0.183423
## site106 -0.361471 0.696651 0.218776 0.188760 -1.915 0.055495 .
## site107 0.014799 1.014909 0.240825 0.273165 0.054 0.956794
## site108 -0.138058 0.871048 0.240266 0.446123 -0.309 0.756969
## site10R -0.268206 0.764750 0.290010 0.191852 -1.398 0.162119
## site11R -0.053798 0.947624 0.238982 0.053304 -1.009 0.312844
## site12R -0.127241 0.880522 0.252414 0.035453 -3.589 0.000332 ***
## site13R -0.379684 0.684078 0.240514 0.182455 -2.081 0.037436 *
## site14R 0.113967 1.120715 0.395747 0.161881 0.704 0.481422
## site15R 0.035208 1.035836 0.270731 0.044529 0.791 0.429133
## site16R 0.145498 1.156615 0.263200 0.061852 2.352 0.018654 *
## site17R -0.061862 0.940013 0.275274 0.015896 -3.892 9.96e-05 ***
## site18R -0.428079 0.651760 0.259541 0.268016 -1.597 0.110218
## site19R -0.105090 0.900243 0.283019 0.098447 -1.067 0.285757
## site20R -0.224421 0.798979 0.287237 0.022349 -10.042 < 2e-16 ***
## site21R -0.264932 0.767258 0.252680 0.012422 -21.327 < 2e-16 ***
## site22R -0.037611 0.963088 0.247156 0.051551 -0.730 0.465645
## site23R -0.147370 0.862975 0.248186 0.035698 -4.128 3.66e-05 ***
## site24R 0.009953 1.010003 0.248815 0.059968 0.166 0.868178
## site25R -0.106802 0.898703 0.250535 0.098460 -1.085 0.278043
## site26R -0.360775 0.697136 0.265510 0.146371 -2.465 0.013709 *
## site27R -0.163213 0.849411 0.369311 0.081476 -2.003 0.045156 *
## site28R -0.040415 0.960391 0.265558 0.032526 -1.243 0.214037
## site29R -0.341701 0.710561 0.334928 0.131875 -2.591 0.009567 **
## site30R -0.276499 0.758435 0.255172 0.174497 -1.585 0.113069
## site31R -0.197409 0.820855 0.270637 0.066663 -2.961 0.003063 **
## site32R -0.422961 0.655104 0.267957 0.139428 -3.034 0.002417 **
## site33R -0.294912 0.744597 0.257519 0.096918 -3.043 0.002343 **
## site34R -0.222286 0.800687 0.247708 0.120158 -1.850 0.064321 .
## site35R -0.078901 0.924132 0.264319 0.071229 -1.108 0.267990
## site36R -0.206354 0.813545 0.254537 0.017977 -11.479 < 2e-16 ***
## site37R -0.240822 0.785981 0.256713 0.142297 -1.692 0.090572 .
## site38R -0.275536 0.759165 0.261154 0.053601 -5.140 2.74e-07 ***
## site39R -0.260609 0.770583 0.289711 0.093779 -2.779 0.005453 **
## site40R 0.085396 1.089148 0.267604 0.149285 0.572 0.567299
## site41R -0.260113 0.770965 0.251686 0.094473 -2.753 0.005900 **
## site42R 0.136753 1.146545 0.275260 0.016870 8.106 5.21e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## OM 0.9814 1.0189 0.9156 1.0519
## dx_yr 1.2439 0.8039 0.9237 1.6751
## site02R 1.6214 0.6167 1.2321 2.1339
## site03R 0.8038 1.2440 0.6620 0.9761
## site04R 0.8276 1.2084 0.6976 0.9818
## site05R 0.8410 1.1891 0.7803 0.9064
## site06R 1.1105 0.9005 0.8807 1.4004
## site07R 0.6599 1.5155 0.4592 0.9482
## site09R 0.9302 1.0750 0.7793 1.1104
## site1 0.8962 1.1158 0.7540 1.0652
## site101 0.6897 1.4499 0.4222 1.1266
## site102 0.6369 1.5700 0.4200 0.9659
## site103 0.8506 1.1756 0.6303 1.1480
## site104 0.7165 1.3957 0.5263 0.9754
## site105 0.7346 1.3613 0.4663 1.1572
## site106 0.6967 1.4354 0.4812 1.0085
## site107 1.0149 0.9853 0.5942 1.7336
## site108 0.8710 1.1480 0.3633 2.0882
## site10R 0.7648 1.3076 0.5251 1.1138
## site11R 0.9476 1.0553 0.8536 1.0520
## site12R 0.8805 1.1357 0.8214 0.9439
## site13R 0.6841 1.4618 0.4784 0.9782
## site14R 1.1207 0.8923 0.8160 1.5392
## site15R 1.0358 0.9654 0.9493 1.1303
## site16R 1.1566 0.8646 1.0246 1.3057
## site17R 0.9400 1.0638 0.9112 0.9698
## site18R 0.6518 1.5343 0.3854 1.1021
## site19R 0.9002 1.1108 0.7423 1.0918
## site20R 0.7990 1.2516 0.7647 0.8348
## site21R 0.7673 1.3033 0.7488 0.7862
## site22R 0.9631 1.0383 0.8705 1.0655
## site23R 0.8630 1.1588 0.8047 0.9255
## site24R 1.0100 0.9901 0.8980 1.1360
## site25R 0.8987 1.1127 0.7410 1.0900
## site26R 0.6971 1.4344 0.5233 0.9288
## site27R 0.8494 1.1773 0.7240 0.9965
## site28R 0.9604 1.0412 0.9011 1.0236
## site29R 0.7106 1.4073 0.5487 0.9201
## site30R 0.7584 1.3185 0.5387 1.0677
## site31R 0.8209 1.2182 0.7203 0.9354
## site32R 0.6551 1.5265 0.4985 0.8610
## site33R 0.7446 1.3430 0.6158 0.9004
## site34R 0.8007 1.2489 0.6327 1.0133
## site35R 0.9241 1.0821 0.8037 1.0626
## site36R 0.8135 1.2292 0.7854 0.8427
## site37R 0.7860 1.2723 0.5947 1.0388
## site38R 0.7592 1.3172 0.6835 0.8433
## site39R 0.7706 1.2977 0.6412 0.9261
## site40R 1.0891 0.9181 0.8129 1.4593
## site41R 0.7710 1.2971 0.6406 0.9278
## site42R 1.1465 0.8722 1.1093 1.1851
##
## Concordance= 0.673 (se = 0.067 )
## Likelihood ratio test= 2370 on 51 df, p=<2e-16
## Wald test = 518.4 on 51 df, p=<2e-16
## Score (logrank) test = 1907 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ OM + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ OM + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = All, id = ID,
## cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## OM -0.015514 0.984606 0.013218 0.035472 -0.437 0.661852
## dx_yr 0.215136 1.240030 0.005610 0.155057 1.387 0.165302
## age_dx 0.009982 1.010032 0.001177 0.003947 2.529 0.011447
## sexF -0.142140 0.867500 0.026679 0.059842 -2.375 0.017538
## dich_RaceNon-White 0.008508 1.008544 0.036841 0.028203 0.302 0.762899
## smokeHxFormer 0.102854 1.108329 0.032935 0.062810 1.638 0.101520
## smokeHxAlways 0.046812 1.047925 0.074822 0.020030 2.337 0.019438
## smokeHxUnknown 0.061387 1.063310 0.070527 0.038421 1.598 0.110103
## smokeHxEver -0.006450 0.993571 0.049895 0.013031 -0.495 0.620640
## disadv 0.012674 1.012755 0.045925 0.024629 0.515 0.606831
## site02R 0.467637 1.596218 0.258448 0.128658 3.635 0.000278
## site03R -0.201480 0.817520 0.267526 0.089346 -2.255 0.024129
## site04R -0.137578 0.871467 0.274870 0.087801 -1.567 0.117133
## site05R -0.143354 0.866448 0.281557 0.067067 -2.137 0.032559
## site06R 0.073315 1.076069 0.272683 0.151487 0.484 0.628411
## site07R -0.451129 0.636909 0.249995 0.167214 -2.698 0.006977
## site09R -0.115890 0.890573 0.275521 0.066768 -1.736 0.082614
## site1 -0.164466 0.848347 0.223150 0.057886 -2.841 0.004494
## site101 -0.381233 0.683019 0.227042 0.215548 -1.769 0.076949
## site102 -0.483281 0.616757 0.227195 0.174286 -2.773 0.005556
## site103 -0.192101 0.825223 0.222705 0.111223 -1.727 0.084138
## site104 -0.386610 0.679356 0.228355 0.109416 -3.533 0.000410
## site105 -0.353697 0.702088 0.225163 0.189645 -1.865 0.062175
## site106 -0.455873 0.633894 0.226026 0.133517 -3.414 0.000639
## site107 -0.041749 0.959111 0.247540 0.231878 -0.180 0.857115
## site108 -0.196684 0.821450 0.246534 0.399508 -0.492 0.622496
## site10R -0.310210 0.733293 0.296755 0.176684 -1.756 0.079134
## site11R -0.117228 0.889383 0.245473 0.037089 -3.161 0.001574
## site12R -0.157987 0.853861 0.256945 0.022693 -6.962 3.36e-12
## site13R -0.452055 0.636319 0.245178 0.163219 -2.770 0.005612
## site14R 0.131940 1.141040 0.398621 0.176470 0.748 0.454663
## site15R -0.028651 0.971756 0.275362 0.034480 -0.831 0.406011
## site16R 0.172630 1.188427 0.267366 0.071230 2.424 0.015369
## site17R -0.014834 0.985276 0.279548 0.020565 -0.721 0.470712
## site18R -0.466637 0.627108 0.264184 0.248757 -1.876 0.060673
## site19R -0.139784 0.869546 0.287202 0.075887 -1.842 0.065474
## site20R -0.235590 0.790105 0.291412 0.009749 -24.166 < 2e-16
## site21R -0.304958 0.737154 0.257247 0.021083 -14.465 < 2e-16
## site22R -0.083502 0.919889 0.251856 0.034841 -2.397 0.016546
## site23R -0.170428 0.843303 0.254994 0.012582 -13.546 < 2e-16
## site24R 0.043897 1.044875 0.253560 0.071933 0.610 0.541693
## site25R -0.129850 0.878227 0.256770 0.091032 -1.426 0.153749
## site26R -0.411664 0.662547 0.270086 0.135854 -3.030 0.002444
## site27R -0.142220 0.867430 0.372359 0.073700 -1.930 0.053640
## site28R 0.032543 1.033079 0.270123 0.037966 0.857 0.391348
## site29R -0.366688 0.693026 0.338916 0.122079 -3.004 0.002667
## site30R -0.323406 0.723680 0.260298 0.160519 -2.015 0.043930
## site31R -0.189253 0.827577 0.274758 0.058590 -3.230 0.001237
## site32R -0.415191 0.660214 0.277947 0.121356 -3.421 0.000623
## site33R -0.331322 0.717974 0.262978 0.073612 -4.501 6.77e-06
## site34R -0.257530 0.772959 0.252810 0.104895 -2.455 0.014084
## site35R -0.126847 0.880868 0.270110 0.045929 -2.762 0.005749
## site36R -0.203835 0.815597 0.259132 0.001470 -138.682 < 2e-16
## site37R -0.278190 0.757153 0.261156 0.130070 -2.139 0.032454
## site38R -0.295580 0.744100 0.266684 0.045663 -6.473 9.60e-11
## site39R -0.306678 0.735888 0.293860 0.067064 -4.573 4.81e-06
## site40R 0.032720 1.033261 0.273248 0.122009 0.268 0.788562
## site41R -0.264936 0.767255 0.256055 0.084266 -3.144 0.001666
## site42R 0.141569 1.152079 0.279315 0.028402 4.984 6.21e-07
##
## OM
## dx_yr
## age_dx *
## sexF *
## dich_RaceNon-White
## smokeHxFormer
## smokeHxAlways *
## smokeHxUnknown
## smokeHxEver
## disadv
## site02R ***
## site03R *
## site04R
## site05R *
## site06R
## site07R **
## site09R .
## site1 **
## site101 .
## site102 **
## site103 .
## site104 ***
## site105 .
## site106 ***
## site107
## site108
## site10R .
## site11R **
## site12R ***
## site13R **
## site14R
## site15R
## site16R *
## site17R
## site18R .
## site19R .
## site20R ***
## site21R ***
## site22R *
## site23R ***
## site24R
## site25R
## site26R **
## site27R .
## site28R
## site29R **
## site30R *
## site31R **
## site32R ***
## site33R ***
## site34R *
## site35R **
## site36R ***
## site37R *
## site38R ***
## site39R ***
## site40R
## site41R **
## site42R ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## OM 0.9846 1.0156 0.9185 1.0555
## dx_yr 1.2400 0.8064 0.9151 1.6804
## age_dx 1.0100 0.9901 1.0022 1.0179
## sexF 0.8675 1.1527 0.7715 0.9755
## dich_RaceNon-White 1.0085 0.9915 0.9543 1.0659
## smokeHxFormer 1.1083 0.9023 0.9800 1.2535
## smokeHxAlways 1.0479 0.9543 1.0076 1.0899
## smokeHxUnknown 1.0633 0.9405 0.9862 1.1465
## smokeHxEver 0.9936 1.0065 0.9685 1.0193
## disadv 1.0128 0.9874 0.9650 1.0628
## site02R 1.5962 0.6265 1.2404 2.0540
## site03R 0.8175 1.2232 0.6862 0.9740
## site04R 0.8715 1.1475 0.7337 1.0351
## site05R 0.8664 1.1541 0.7597 0.9882
## site06R 1.0761 0.9293 0.7996 1.4481
## site07R 0.6369 1.5701 0.4589 0.8839
## site09R 0.8906 1.1229 0.7813 1.0151
## site1 0.8483 1.1788 0.7574 0.9503
## site101 0.6830 1.4641 0.4477 1.0421
## site102 0.6168 1.6214 0.4383 0.8679
## site103 0.8252 1.2118 0.6636 1.0262
## site104 0.6794 1.4720 0.5482 0.8418
## site105 0.7021 1.4243 0.4841 1.0182
## site106 0.6339 1.5775 0.4879 0.8235
## site107 0.9591 1.0426 0.6088 1.5109
## site108 0.8215 1.2174 0.3754 1.7974
## site10R 0.7333 1.3637 0.5187 1.0367
## site11R 0.8894 1.1244 0.8270 0.9564
## site12R 0.8539 1.1712 0.8167 0.8927
## site13R 0.6363 1.5715 0.4621 0.8762
## site14R 1.1410 0.8764 0.8074 1.6125
## site15R 0.9718 1.0291 0.9083 1.0397
## site16R 1.1884 0.8414 1.0336 1.3665
## site17R 0.9853 1.0149 0.9464 1.0258
## site18R 0.6271 1.5946 0.3851 1.0211
## site19R 0.8695 1.1500 0.7494 1.0090
## site20R 0.7901 1.2657 0.7752 0.8053
## site21R 0.7372 1.3566 0.7073 0.7683
## site22R 0.9199 1.0871 0.8592 0.9849
## site23R 0.8433 1.1858 0.8228 0.8644
## site24R 1.0449 0.9571 0.9075 1.2031
## site25R 0.8782 1.1387 0.7347 1.0498
## site26R 0.6625 1.5093 0.5077 0.8647
## site27R 0.8674 1.1528 0.7508 1.0022
## site28R 1.0331 0.9680 0.9590 1.1129
## site29R 0.6930 1.4429 0.5456 0.8804
## site30R 0.7237 1.3818 0.5283 0.9912
## site31R 0.8276 1.2083 0.7378 0.9283
## site32R 0.6602 1.5147 0.5205 0.8375
## site33R 0.7180 1.3928 0.6215 0.8294
## site34R 0.7730 1.2937 0.6293 0.9494
## site35R 0.8809 1.1352 0.8050 0.9638
## site36R 0.8156 1.2261 0.8133 0.8180
## site37R 0.7572 1.3207 0.5868 0.9770
## site38R 0.7441 1.3439 0.6804 0.8138
## site39R 0.7359 1.3589 0.6452 0.8393
## site40R 1.0333 0.9678 0.8135 1.3124
## site41R 0.7673 1.3033 0.6504 0.9050
## site42R 1.1521 0.8680 1.0897 1.2180
##
## Concordance= 0.677 (se = 0.064 )
## Likelihood ratio test= 2537 on 59 df, p=<2e-16
## Wald test = 446.8 on 59 df, p=<2e-16
## Score (logrank) test = 2066 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
summary(All$OM)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.1262 2.3201 2.9292 3.0532 3.7305 10.8459 688
IQR(All$OM, na.rm=T)
## [1] 1.410373
# Will use the 5yr pre-censoring IQR (1.270977), not this one
All <- All %>% mutate(OM_IQR = OM/1.270977)
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ OM_IQR + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ OM_IQR + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All,
## id = ID, cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## OM_IQR -0.019718 0.980475 0.016800 0.045084 -0.437 0.661852
## dx_yr 0.215136 1.240030 0.005610 0.155057 1.387 0.165302
## age_dx 0.009982 1.010032 0.001177 0.003947 2.529 0.011447
## sexF -0.142140 0.867500 0.026679 0.059842 -2.375 0.017538
## dich_RaceNon-White 0.008508 1.008544 0.036841 0.028203 0.302 0.762899
## smokeHxFormer 0.102854 1.108329 0.032935 0.062810 1.638 0.101520
## smokeHxAlways 0.046812 1.047925 0.074822 0.020030 2.337 0.019438
## smokeHxUnknown 0.061387 1.063310 0.070527 0.038421 1.598 0.110103
## smokeHxEver -0.006450 0.993571 0.049895 0.013031 -0.495 0.620640
## disadv 0.012674 1.012755 0.045925 0.024629 0.515 0.606831
## site02R 0.467637 1.596218 0.258448 0.128658 3.635 0.000278
## site03R -0.201480 0.817520 0.267526 0.089346 -2.255 0.024129
## site04R -0.137578 0.871467 0.274870 0.087801 -1.567 0.117133
## site05R -0.143354 0.866448 0.281557 0.067067 -2.137 0.032559
## site06R 0.073315 1.076069 0.272683 0.151487 0.484 0.628411
## site07R -0.451129 0.636909 0.249995 0.167214 -2.698 0.006977
## site09R -0.115890 0.890573 0.275521 0.066768 -1.736 0.082614
## site1 -0.164466 0.848347 0.223150 0.057886 -2.841 0.004494
## site101 -0.381233 0.683019 0.227042 0.215548 -1.769 0.076949
## site102 -0.483281 0.616757 0.227195 0.174286 -2.773 0.005556
## site103 -0.192101 0.825223 0.222705 0.111223 -1.727 0.084138
## site104 -0.386610 0.679356 0.228355 0.109416 -3.533 0.000410
## site105 -0.353697 0.702088 0.225163 0.189645 -1.865 0.062175
## site106 -0.455873 0.633894 0.226026 0.133517 -3.414 0.000639
## site107 -0.041749 0.959111 0.247540 0.231878 -0.180 0.857115
## site108 -0.196684 0.821450 0.246534 0.399508 -0.492 0.622496
## site10R -0.310210 0.733293 0.296755 0.176684 -1.756 0.079134
## site11R -0.117228 0.889383 0.245473 0.037089 -3.161 0.001574
## site12R -0.157987 0.853861 0.256945 0.022693 -6.962 3.36e-12
## site13R -0.452055 0.636319 0.245178 0.163219 -2.770 0.005612
## site14R 0.131940 1.141040 0.398621 0.176470 0.748 0.454663
## site15R -0.028651 0.971756 0.275362 0.034480 -0.831 0.406011
## site16R 0.172630 1.188427 0.267366 0.071230 2.424 0.015369
## site17R -0.014834 0.985276 0.279548 0.020565 -0.721 0.470712
## site18R -0.466637 0.627108 0.264184 0.248757 -1.876 0.060673
## site19R -0.139784 0.869546 0.287202 0.075887 -1.842 0.065474
## site20R -0.235590 0.790105 0.291412 0.009749 -24.166 < 2e-16
## site21R -0.304958 0.737154 0.257247 0.021083 -14.465 < 2e-16
## site22R -0.083502 0.919889 0.251856 0.034841 -2.397 0.016546
## site23R -0.170428 0.843303 0.254994 0.012582 -13.546 < 2e-16
## site24R 0.043897 1.044875 0.253560 0.071933 0.610 0.541693
## site25R -0.129850 0.878227 0.256770 0.091032 -1.426 0.153749
## site26R -0.411664 0.662547 0.270086 0.135854 -3.030 0.002444
## site27R -0.142220 0.867430 0.372359 0.073700 -1.930 0.053640
## site28R 0.032543 1.033079 0.270123 0.037966 0.857 0.391348
## site29R -0.366688 0.693026 0.338916 0.122079 -3.004 0.002667
## site30R -0.323406 0.723680 0.260298 0.160519 -2.015 0.043930
## site31R -0.189253 0.827577 0.274758 0.058590 -3.230 0.001237
## site32R -0.415191 0.660214 0.277947 0.121356 -3.421 0.000623
## site33R -0.331322 0.717974 0.262978 0.073612 -4.501 6.77e-06
## site34R -0.257530 0.772959 0.252810 0.104895 -2.455 0.014084
## site35R -0.126847 0.880868 0.270110 0.045929 -2.762 0.005749
## site36R -0.203835 0.815597 0.259132 0.001470 -138.682 < 2e-16
## site37R -0.278190 0.757153 0.261156 0.130070 -2.139 0.032454
## site38R -0.295580 0.744100 0.266684 0.045663 -6.473 9.60e-11
## site39R -0.306678 0.735888 0.293860 0.067064 -4.573 4.81e-06
## site40R 0.032720 1.033261 0.273248 0.122009 0.268 0.788562
## site41R -0.264936 0.767255 0.256055 0.084266 -3.144 0.001666
## site42R 0.141569 1.152079 0.279315 0.028402 4.984 6.21e-07
##
## OM_IQR
## dx_yr
## age_dx *
## sexF *
## dich_RaceNon-White
## smokeHxFormer
## smokeHxAlways *
## smokeHxUnknown
## smokeHxEver
## disadv
## site02R ***
## site03R *
## site04R
## site05R *
## site06R
## site07R **
## site09R .
## site1 **
## site101 .
## site102 **
## site103 .
## site104 ***
## site105 .
## site106 ***
## site107
## site108
## site10R .
## site11R **
## site12R ***
## site13R **
## site14R
## site15R
## site16R *
## site17R
## site18R .
## site19R .
## site20R ***
## site21R ***
## site22R *
## site23R ***
## site24R
## site25R
## site26R **
## site27R .
## site28R
## site29R **
## site30R *
## site31R **
## site32R ***
## site33R ***
## site34R *
## site35R **
## site36R ***
## site37R *
## site38R ***
## site39R ***
## site40R
## site41R **
## site42R ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## OM_IQR 0.9805 1.0199 0.8976 1.0711
## dx_yr 1.2400 0.8064 0.9151 1.6804
## age_dx 1.0100 0.9901 1.0022 1.0179
## sexF 0.8675 1.1527 0.7715 0.9755
## dich_RaceNon-White 1.0085 0.9915 0.9543 1.0659
## smokeHxFormer 1.1083 0.9023 0.9800 1.2535
## smokeHxAlways 1.0479 0.9543 1.0076 1.0899
## smokeHxUnknown 1.0633 0.9405 0.9862 1.1465
## smokeHxEver 0.9936 1.0065 0.9685 1.0193
## disadv 1.0128 0.9874 0.9650 1.0628
## site02R 1.5962 0.6265 1.2404 2.0540
## site03R 0.8175 1.2232 0.6862 0.9740
## site04R 0.8715 1.1475 0.7337 1.0351
## site05R 0.8664 1.1541 0.7597 0.9882
## site06R 1.0761 0.9293 0.7996 1.4481
## site07R 0.6369 1.5701 0.4589 0.8839
## site09R 0.8906 1.1229 0.7813 1.0151
## site1 0.8483 1.1788 0.7574 0.9503
## site101 0.6830 1.4641 0.4477 1.0421
## site102 0.6168 1.6214 0.4383 0.8679
## site103 0.8252 1.2118 0.6636 1.0262
## site104 0.6794 1.4720 0.5482 0.8418
## site105 0.7021 1.4243 0.4841 1.0182
## site106 0.6339 1.5775 0.4879 0.8235
## site107 0.9591 1.0426 0.6088 1.5109
## site108 0.8215 1.2174 0.3754 1.7974
## site10R 0.7333 1.3637 0.5187 1.0367
## site11R 0.8894 1.1244 0.8270 0.9564
## site12R 0.8539 1.1712 0.8167 0.8927
## site13R 0.6363 1.5715 0.4621 0.8762
## site14R 1.1410 0.8764 0.8074 1.6125
## site15R 0.9718 1.0291 0.9083 1.0397
## site16R 1.1884 0.8414 1.0336 1.3665
## site17R 0.9853 1.0149 0.9464 1.0258
## site18R 0.6271 1.5946 0.3851 1.0211
## site19R 0.8695 1.1500 0.7494 1.0090
## site20R 0.7901 1.2657 0.7752 0.8053
## site21R 0.7372 1.3566 0.7073 0.7683
## site22R 0.9199 1.0871 0.8592 0.9849
## site23R 0.8433 1.1858 0.8228 0.8644
## site24R 1.0449 0.9571 0.9075 1.2031
## site25R 0.8782 1.1387 0.7347 1.0498
## site26R 0.6625 1.5093 0.5077 0.8647
## site27R 0.8674 1.1528 0.7508 1.0022
## site28R 1.0331 0.9680 0.9590 1.1129
## site29R 0.6930 1.4429 0.5456 0.8804
## site30R 0.7237 1.3818 0.5283 0.9912
## site31R 0.8276 1.2083 0.7378 0.9283
## site32R 0.6602 1.5147 0.5205 0.8375
## site33R 0.7180 1.3928 0.6215 0.8294
## site34R 0.7730 1.2937 0.6293 0.9494
## site35R 0.8809 1.1352 0.8050 0.9638
## site36R 0.8156 1.2261 0.8133 0.8180
## site37R 0.7572 1.3207 0.5868 0.9770
## site38R 0.7441 1.3439 0.6804 0.8138
## site39R 0.7359 1.3589 0.6452 0.8393
## site40R 1.0333 0.9678 0.8135 1.3124
## site41R 0.7673 1.3033 0.6504 0.9050
## site42R 1.1521 0.8680 1.0897 1.2180
##
## Concordance= 0.677 (se = 0.064 )
## Likelihood ratio test= 2537 on 59 df, p=<2e-16
## Wald test = 446.8 on 59 df, p=<2e-16
## Score (logrank) test = 2066 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
So this indicates that there is a HR of 0.98 per IQR increase in OM as compared with a HR of 0.98 per 1ug/m3 increase in OM.
Base model
#First need to make dataframe that only includes patients with a value for event
Allx <- All %>% filter(!is.na(OM) & !is.na(deadORtx) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(cohort) & !is.na(site) & OM<20)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$OM, df=3) + Allx$dx_yr + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$OM, exp(predicted$fit), type="n")
lines(sm.spline(Allx$OM, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$OM, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$OM, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
Complete model
#First need to make dataframe that only includes patients with time_DeathTxCensor
Allx <- All %>% filter(!is.na(OM) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(deadORtx) & !is.na(age_dx) & !is.na(sex) & !is.na(smokeHx) & !is.na(dich_Race) & !is.na(disadv) & !is.na(site) & OM<20)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$OM, df=3) + Allx$dx_yr + Allx$age_dx + Allx$sex + Allx$smokeHx + Allx$dich_Race + Allx$disadv + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$OM, exp(predicted$fit), type="n")
lines(sm.spline(Allx$OM, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$OM, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$OM, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ SS + dx_yr + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SS + dx_yr + site,
## data = All, id = ID, cluster = cohort)
##
## n= 335367, number of events= 6459
## (1981 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## SS 0.020497 1.020708 0.094091 0.292279 0.070 0.944092
## dx_yr 0.218675 1.244426 0.005446 0.153801 1.422 0.155084
## site02R 0.451455 1.570596 0.277807 0.208896 2.161 0.030684 *
## site03R -0.215314 0.806289 0.263528 0.131153 -1.642 0.100653
## site04R -0.187276 0.829215 0.270664 0.093410 -2.005 0.044976 *
## site05R -0.183586 0.832280 0.275726 0.034778 -5.279 1.30e-07 ***
## site06R 0.098593 1.103617 0.267155 0.052636 1.873 0.061051 .
## site07R -0.418419 0.658086 0.245434 0.206932 -2.022 0.043175 *
## site09R -0.072048 0.930486 0.269305 0.067387 -1.069 0.284990
## site1 -0.114678 0.891653 0.215741 0.127950 -0.896 0.370105
## site101 -0.370024 0.690718 0.220332 0.272609 -1.357 0.174673
## site102 -0.445296 0.640634 0.221248 0.279195 -1.595 0.110728
## site103 -0.163740 0.848963 0.215499 0.158340 -1.034 0.301087
## site104 -0.340244 0.711596 0.221295 0.178542 -1.906 0.056691 .
## site105 -0.319591 0.726446 0.217530 0.275479 -1.160 0.245997
## site106 -0.368383 0.691852 0.218759 0.216263 -1.703 0.088493 .
## site107 0.005055 1.005068 0.240814 0.313941 0.016 0.987153
## site108 -0.125552 0.882009 0.241458 0.503898 -0.249 0.803235
## site10R -0.265710 0.766661 0.298862 0.083472 -3.183 0.001456 **
## site11R -0.076462 0.926388 0.238459 0.108904 -0.702 0.482616
## site12R -0.111015 0.894926 0.252962 0.065656 -1.691 0.090864 .
## site13R -0.380287 0.683665 0.240591 0.203923 -1.865 0.062202 .
## site14R 0.117091 1.124222 0.395749 0.178262 0.657 0.511276
## site15R 0.022114 1.022360 0.271056 0.022713 0.974 0.330260
## site16R 0.141641 1.152163 0.263384 0.090836 1.559 0.118924
## site17R -0.063763 0.938227 0.275689 0.069463 -0.918 0.358651
## site18R -0.441491 0.643077 0.259948 0.236392 -1.868 0.061815 .
## site19R -0.084947 0.918561 0.282927 0.094557 -0.898 0.368990
## site20R -0.218766 0.803510 0.287767 0.068424 -3.197 0.001388 **
## site21R -0.283005 0.753516 0.252429 0.062441 -4.532 5.83e-06 ***
## site22R -0.040274 0.960527 0.247285 0.029654 -1.358 0.174434
## site23R -0.150835 0.859989 0.248180 0.047534 -3.173 0.001508 **
## site24R 0.022773 1.023034 0.249230 0.032946 0.691 0.489418
## site25R -0.138316 0.870824 0.270341 0.210071 -0.658 0.510266
## site26R -0.366130 0.693413 0.269581 0.040022 -9.148 < 2e-16 ***
## site27R -0.158290 0.853602 0.369315 0.081007 -1.954 0.050696 .
## site28R -0.048549 0.952611 0.265782 0.090530 -0.536 0.591770
## site29R -0.341095 0.710991 0.336781 0.033922 -10.055 < 2e-16 ***
## site30R -0.279651 0.756048 0.255174 0.189059 -1.479 0.139094
## site31R -0.191355 0.825840 0.270954 0.098655 -1.940 0.052424 .
## site32R -0.422518 0.655394 0.267986 0.150555 -2.806 0.005010 **
## site33R -0.295941 0.743831 0.258600 0.028789 -10.280 < 2e-16 ***
## site34R -0.215616 0.806044 0.247820 0.133272 -1.618 0.105690
## site35R -0.075115 0.927637 0.264363 0.038549 -1.949 0.051348 .
## site36R -0.217525 0.804507 0.254571 0.068009 -3.198 0.001381 **
## site37R -0.243247 0.784078 0.256762 0.164639 -1.477 0.139552
## site38R -0.279114 0.756454 0.261190 0.077336 -3.609 0.000307 ***
## site39R -0.258297 0.772366 0.290266 0.148974 -1.734 0.082948 .
## site40R 0.033132 1.033687 0.267245 0.149033 0.222 0.824071
## site41R -0.266855 0.765784 0.251833 0.073843 -3.614 0.000302 ***
## site42R 0.135382 1.144974 0.275270 0.006286 21.536 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SS 1.0207 0.9797 0.5756 1.8100
## dx_yr 1.2444 0.8036 0.9206 1.6822
## site02R 1.5706 0.6367 1.0429 2.3653
## site03R 0.8063 1.2403 0.6235 1.0426
## site04R 0.8292 1.2060 0.6905 0.9958
## site05R 0.8323 1.2015 0.7774 0.8910
## site06R 1.1036 0.9061 0.9954 1.2236
## site07R 0.6581 1.5196 0.4387 0.9872
## site09R 0.9305 1.0747 0.8154 1.0619
## site1 0.8917 1.1215 0.6939 1.1458
## site101 0.6907 1.4478 0.4048 1.1785
## site102 0.6406 1.5610 0.3706 1.1073
## site103 0.8490 1.1779 0.6225 1.1579
## site104 0.7116 1.4053 0.5015 1.0097
## site105 0.7264 1.3766 0.4234 1.2465
## site106 0.6919 1.4454 0.4528 1.0571
## site107 1.0051 0.9950 0.5432 1.8596
## site108 0.8820 1.1338 0.3285 2.3681
## site10R 0.7667 1.3044 0.6510 0.9029
## site11R 0.9264 1.0795 0.7483 1.1468
## site12R 0.8949 1.1174 0.7869 1.0178
## site13R 0.6837 1.4627 0.4584 1.0196
## site14R 1.1242 0.8895 0.7927 1.5944
## site15R 1.0224 0.9781 0.9778 1.0689
## site16R 1.1522 0.8679 0.9643 1.3767
## site17R 0.9382 1.0658 0.8188 1.0751
## site18R 0.6431 1.5550 0.4046 1.0221
## site19R 0.9186 1.0887 0.7632 1.1056
## site20R 0.8035 1.2445 0.7027 0.9188
## site21R 0.7535 1.3271 0.6667 0.8516
## site22R 0.9605 1.0411 0.9063 1.0180
## site23R 0.8600 1.1628 0.7835 0.9440
## site24R 1.0230 0.9775 0.9591 1.0913
## site25R 0.8708 1.1483 0.5769 1.3144
## site26R 0.6934 1.4421 0.6411 0.7500
## site27R 0.8536 1.1715 0.7283 1.0005
## site28R 0.9526 1.0497 0.7977 1.1376
## site29R 0.7110 1.4065 0.6653 0.7599
## site30R 0.7560 1.3227 0.5219 1.0952
## site31R 0.8258 1.2109 0.6806 1.0020
## site32R 0.6554 1.5258 0.4879 0.8803
## site33R 0.7438 1.3444 0.7030 0.7870
## site34R 0.8060 1.2406 0.6208 1.0466
## site35R 0.9276 1.0780 0.8601 1.0004
## site36R 0.8045 1.2430 0.7041 0.9192
## site37R 0.7841 1.2754 0.5678 1.0827
## site38R 0.7565 1.3220 0.6501 0.8803
## site39R 0.7724 1.2947 0.5768 1.0343
## site40R 1.0337 0.9674 0.7718 1.3843
## site41R 0.7658 1.3059 0.6626 0.8850
## site42R 1.1450 0.8734 1.1310 1.1592
##
## Concordance= 0.673 (se = 0.067 )
## Likelihood ratio test= 2368 on 51 df, p=<2e-16
## Wald test = 43.91 on 51 df, p=0.7
## Score (logrank) test = 1916 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ SS + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SS + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = All, id = ID,
## cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## SS -0.003421 0.996585 0.095956 0.288188 -0.012 0.99053
## dx_yr 0.215533 1.240523 0.005608 0.156930 1.373 0.16962
## age_dx 0.009958 1.010007 0.001177 0.003922 2.539 0.01113 *
## sexF -0.141662 0.867914 0.026679 0.060851 -2.328 0.01991 *
## dich_RaceNon-White 0.005131 1.005144 0.036820 0.026930 0.191 0.84890
## smokeHxFormer 0.105476 1.111239 0.032878 0.053466 1.973 0.04852 *
## smokeHxAlways 0.048142 1.049320 0.074843 0.027627 1.743 0.08141 .
## smokeHxUnknown 0.061380 1.063303 0.070543 0.038375 1.599 0.10971
## smokeHxEver -0.006733 0.993290 0.049901 0.013133 -0.513 0.60819
## disadv 0.004896 1.004908 0.045440 0.042582 0.115 0.90846
## site02R 0.463006 1.588843 0.283019 0.202948 2.281 0.02252 *
## site03R -0.204403 0.815133 0.267780 0.125842 -1.624 0.10432
## site04R -0.140085 0.869284 0.274869 0.097138 -1.442 0.14926
## site05R -0.157114 0.854606 0.281673 0.009075 -17.314 < 2e-16 ***
## site06R 0.063789 1.065867 0.272947 0.090322 0.706 0.48004
## site07R -0.457045 0.633152 0.249967 0.188675 -2.422 0.01542 *
## site09R -0.116798 0.889765 0.275657 0.042175 -2.769 0.00562 **
## site1 -0.174802 0.839623 0.223111 0.097668 -1.790 0.07349 .
## site101 -0.385609 0.680036 0.227107 0.243254 -1.585 0.11292
## site102 -0.489002 0.613238 0.228233 0.251062 -1.948 0.05145 .
## site103 -0.196579 0.821537 0.222664 0.120906 -1.626 0.10398
## site104 -0.396062 0.672965 0.228221 0.135455 -2.924 0.00346 **
## site105 -0.369283 0.691230 0.224872 0.240335 -1.537 0.12441
## site106 -0.467452 0.626597 0.225840 0.168203 -2.779 0.00545 **
## site107 -0.054793 0.946681 0.247379 0.277989 -0.197 0.84374
## site108 -0.195814 0.822165 0.247665 0.464570 -0.421 0.67339
## site10R -0.294130 0.745179 0.306458 0.092343 -3.185 0.00145 **
## site11R -0.138452 0.870705 0.244822 0.091878 -1.507 0.13183
## site12R -0.151133 0.859733 0.257411 0.050688 -2.982 0.00287 **
## site13R -0.457266 0.633012 0.245183 0.186741 -2.449 0.01434 *
## site14R 0.131260 1.140264 0.398650 0.189175 0.694 0.48777
## site15R -0.037164 0.963518 0.275815 0.023588 -1.576 0.11513
## site16R 0.168619 1.183669 0.267615 0.097843 1.723 0.08482 .
## site17R -0.021922 0.978316 0.279824 0.065434 -0.335 0.73760
## site18R -0.474957 0.621912 0.264707 0.212787 -2.232 0.02561 *
## site19R -0.128027 0.879830 0.287125 0.070387 -1.819 0.06893 .
## site20R -0.237786 0.788372 0.291829 0.058812 -4.043 5.27e-05 ***
## site21R -0.323098 0.723903 0.256867 0.047140 -6.854 7.18e-12 ***
## site22R -0.087242 0.916455 0.252042 0.016525 -5.279 1.30e-07 ***
## site23R -0.174929 0.839517 0.254969 0.023360 -7.488 6.97e-14 ***
## site24R 0.047989 1.049159 0.253908 0.046753 1.026 0.30469
## site25R -0.136346 0.872541 0.277087 0.207245 -0.658 0.51060
## site26R -0.406370 0.666064 0.274476 0.041580 -9.773 < 2e-16 ***
## site27R -0.141574 0.867991 0.372362 0.075396 -1.878 0.06042 .
## site28R 0.022352 1.022603 0.270262 0.088201 0.253 0.79995
## site29R -0.363318 0.695365 0.341137 0.019319 -18.806 < 2e-16 ***
## site30R -0.328410 0.720067 0.260267 0.174927 -1.877 0.06046 .
## site31R -0.189745 0.827170 0.275005 0.092108 -2.060 0.03940 *
## site32R -0.417309 0.658817 0.277950 0.130516 -3.197 0.00139 **
## site33R -0.328079 0.720306 0.264237 0.027493 -11.933 < 2e-16 ***
## site34R -0.256751 0.773561 0.252885 0.118765 -2.162 0.03063 *
## site35R -0.125270 0.882258 0.270239 0.018260 -6.860 6.87e-12 ***
## site36R -0.217007 0.804924 0.259036 0.053442 -4.061 4.89e-05 ***
## site37R -0.284064 0.752718 0.261139 0.152718 -1.860 0.06288 .
## site38R -0.301446 0.739748 0.266659 0.067173 -4.488 7.20e-06 ***
## site39R -0.311576 0.732292 0.294282 0.121934 -2.555 0.01061 *
## site40R -0.006397 0.993623 0.272799 0.122780 -0.052 0.95845
## site41R -0.270591 0.762929 0.256254 0.062240 -4.348 1.38e-05 ***
## site42R 0.137083 1.146923 0.279290 0.017544 7.814 5.55e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SS 0.9966 1.0034 0.5665 1.7532
## dx_yr 1.2405 0.8061 0.9121 1.6873
## age_dx 1.0100 0.9901 1.0023 1.0178
## sexF 0.8679 1.1522 0.7703 0.9779
## dich_RaceNon-White 1.0051 0.9949 0.9535 1.0596
## smokeHxFormer 1.1112 0.8999 1.0007 1.2340
## smokeHxAlways 1.0493 0.9530 0.9940 1.1077
## smokeHxUnknown 1.0633 0.9405 0.9863 1.1464
## smokeHxEver 0.9933 1.0068 0.9680 1.0192
## disadv 1.0049 0.9951 0.9244 1.0924
## site02R 1.5888 0.6294 1.0674 2.3650
## site03R 0.8151 1.2268 0.6370 1.0431
## site04R 0.8693 1.1504 0.7186 1.0516
## site05R 0.8546 1.1701 0.8395 0.8699
## site06R 1.0659 0.9382 0.8929 1.2723
## site07R 0.6332 1.5794 0.4374 0.9164
## site09R 0.8898 1.1239 0.8192 0.9664
## site1 0.8396 1.1910 0.6933 1.0168
## site101 0.6800 1.4705 0.4222 1.0954
## site102 0.6132 1.6307 0.3749 1.0031
## site103 0.8215 1.2172 0.6482 1.0412
## site104 0.6730 1.4860 0.5161 0.8776
## site105 0.6912 1.4467 0.4316 1.1071
## site106 0.6266 1.5959 0.4506 0.8713
## site107 0.9467 1.0563 0.5490 1.6324
## site108 0.8222 1.2163 0.3308 2.0436
## site10R 0.7452 1.3420 0.6218 0.8930
## site11R 0.8707 1.1485 0.7272 1.0425
## site12R 0.8597 1.1632 0.7784 0.9495
## site13R 0.6330 1.5797 0.4390 0.9128
## site14R 1.1403 0.8770 0.7870 1.6521
## site15R 0.9635 1.0379 0.9200 1.0091
## site16R 1.1837 0.8448 0.9771 1.4339
## site17R 0.9783 1.0222 0.8606 1.1122
## site18R 0.6219 1.6079 0.4098 0.9437
## site19R 0.8798 1.1366 0.7665 1.0100
## site20R 0.7884 1.2684 0.7025 0.8847
## site21R 0.7239 1.3814 0.6600 0.7940
## site22R 0.9165 1.0912 0.8872 0.9466
## site23R 0.8395 1.1912 0.8019 0.8788
## site24R 1.0492 0.9531 0.9573 1.1498
## site25R 0.8725 1.1461 0.5813 1.3098
## site26R 0.6661 1.5014 0.6139 0.7226
## site27R 0.8680 1.1521 0.7488 1.0062
## site28R 1.0226 0.9779 0.8603 1.2156
## site29R 0.6954 1.4381 0.6695 0.7222
## site30R 0.7201 1.3888 0.5111 1.0145
## site31R 0.8272 1.2089 0.6905 0.9908
## site32R 0.6588 1.5179 0.5101 0.8509
## site33R 0.7203 1.3883 0.6825 0.7602
## site34R 0.7736 1.2927 0.6129 0.9763
## site35R 0.8823 1.1335 0.8512 0.9144
## site36R 0.8049 1.2424 0.7249 0.8938
## site37R 0.7527 1.3285 0.5580 1.0154
## site38R 0.7397 1.3518 0.6485 0.8438
## site39R 0.7323 1.3656 0.5766 0.9300
## site40R 0.9936 1.0064 0.7811 1.2640
## site41R 0.7629 1.3107 0.6753 0.8619
## site42R 1.1469 0.8719 1.1082 1.1870
##
## Concordance= 0.676 (se = 0.064 )
## Likelihood ratio test= 2535 on 59 df, p=<2e-16
## Wald test = 137.4 on 59 df, p=3e-08
## Score (logrank) test = 2074 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
summary(All$SS)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.1591 0.2311 0.2601 0.2982 2.7091 688
IQR(All$SS, na.rm=T)
## [1] 0.1391352
# Will use the 5yr pre-censoring IQR (0.135), not this one
All <- All %>% mutate(SS_IQR = SS/0.135)
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ SS_IQR + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SS_IQR + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All,
## id = ID, cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## SS_IQR -0.0004618 0.9995383 0.0129541 0.0389053 -0.012 0.99053
## dx_yr 0.2155331 1.2405230 0.0056076 0.1569298 1.373 0.16962
## age_dx 0.0099576 1.0100074 0.0011774 0.0039223 2.539 0.01113
## sexF -0.1416622 0.8679144 0.0266794 0.0608514 -2.328 0.01991
## dich_RaceNon-White 0.0051309 1.0051441 0.0368199 0.0269303 0.191 0.84890
## smokeHxFormer 0.1054756 1.1112390 0.0328780 0.0534662 1.973 0.04852
## smokeHxAlways 0.0481420 1.0493197 0.0748429 0.0276273 1.743 0.08141
## smokeHxUnknown 0.0613803 1.0633032 0.0705428 0.0383751 1.599 0.10971
## smokeHxEver -0.0067328 0.9932898 0.0499011 0.0131331 -0.513 0.60819
## disadv 0.0048959 1.0049079 0.0454403 0.0425824 0.115 0.90846
## site02R 0.4630059 1.5888427 0.2830187 0.2029480 2.281 0.02252
## site03R -0.2044034 0.8151335 0.2677797 0.1258423 -1.624 0.10432
## site04R -0.1400855 0.8692839 0.2748688 0.0971376 -1.442 0.14926
## site05R -0.1571143 0.8546064 0.2816726 0.0090746 -17.314 < 2e-16
## site06R 0.0637889 1.0658674 0.2729475 0.0903224 0.706 0.48004
## site07R -0.4570452 0.6331517 0.2499672 0.1886746 -2.422 0.01542
## site09R -0.1167980 0.8897649 0.2756567 0.0421748 -2.769 0.00562
## site1 -0.1748022 0.8396231 0.2231107 0.0976683 -1.790 0.07349
## site101 -0.3856090 0.6800363 0.2271067 0.2432541 -1.585 0.11292
## site102 -0.4890022 0.6132380 0.2282333 0.2510621 -1.948 0.05145
## site103 -0.1965789 0.8215365 0.2226635 0.1209063 -1.626 0.10398
## site104 -0.3960621 0.6729649 0.2282211 0.1354551 -2.924 0.00346
## site105 -0.3692825 0.6912301 0.2248719 0.2403348 -1.537 0.12441
## site106 -0.4674516 0.6265970 0.2258402 0.1682031 -2.779 0.00545
## site107 -0.0547933 0.9466808 0.2473794 0.2779894 -0.197 0.84374
## site108 -0.1958140 0.8221651 0.2476652 0.4645696 -0.421 0.67339
## site10R -0.2941303 0.7451794 0.3064579 0.0923428 -3.185 0.00145
## site11R -0.1384518 0.8707053 0.2448223 0.0918779 -1.507 0.13183
## site12R -0.1511328 0.8597335 0.2574114 0.0506885 -2.982 0.00287
## site13R -0.4572663 0.6330117 0.2451828 0.1867408 -2.449 0.01434
## site14R 0.1312602 1.1402645 0.3986498 0.1891748 0.694 0.48777
## site15R -0.0371644 0.9635177 0.2758154 0.0235884 -1.576 0.11513
## site16R 0.1686187 1.1836688 0.2676154 0.0978428 1.723 0.08482
## site17R -0.0219222 0.9783164 0.2798241 0.0654340 -0.335 0.73760
## site18R -0.4749573 0.6219116 0.2647071 0.2127873 -2.232 0.02561
## site19R -0.1280266 0.8798300 0.2871248 0.0703868 -1.819 0.06893
## site20R -0.2377858 0.7883716 0.2918292 0.0588122 -4.043 5.27e-05
## site21R -0.3230979 0.7239030 0.2568667 0.0471397 -6.854 7.18e-12
## site22R -0.0872419 0.9164554 0.2520417 0.0165252 -5.279 1.30e-07
## site23R -0.1749289 0.8395167 0.2549691 0.0233601 -7.488 6.97e-14
## site24R 0.0479893 1.0491594 0.2539084 0.0467534 1.026 0.30469
## site25R -0.1363458 0.8725409 0.2770867 0.2072452 -0.658 0.51060
## site26R -0.4063702 0.6660636 0.2744757 0.0415799 -9.773 < 2e-16
## site27R -0.1415738 0.8679911 0.3723620 0.0753958 -1.878 0.06042
## site28R 0.0223515 1.0226032 0.2702615 0.0882014 0.253 0.79995
## site29R -0.3633181 0.6953652 0.3411375 0.0193192 -18.806 < 2e-16
## site30R -0.3284104 0.7200674 0.2602675 0.1749272 -1.877 0.06046
## site31R -0.1897447 0.8271703 0.2750048 0.0921078 -2.060 0.03940
## site32R -0.4173091 0.6588172 0.2779502 0.1305162 -3.197 0.00139
## site33R -0.3280791 0.7203061 0.2642367 0.0274929 -11.933 < 2e-16
## site34R -0.2567508 0.7735609 0.2528849 0.1187646 -2.162 0.03063
## site35R -0.1252702 0.8822585 0.2702391 0.0182603 -6.860 6.87e-12
## site36R -0.2170070 0.8049243 0.2590360 0.0534418 -4.061 4.89e-05
## site37R -0.2840644 0.7527182 0.2611385 0.1527177 -1.860 0.06288
## site38R -0.3014462 0.7397476 0.2666595 0.0671730 -4.488 7.20e-06
## site39R -0.3115761 0.7322919 0.2942818 0.1219339 -2.555 0.01061
## site40R -0.0063972 0.9936232 0.2727986 0.1227797 -0.052 0.95845
## site41R -0.2705906 0.7629288 0.2562540 0.0622400 -4.348 1.38e-05
## site42R 0.1370830 1.1469233 0.2792899 0.0175439 7.814 5.55e-15
##
## SS_IQR
## dx_yr
## age_dx *
## sexF *
## dich_RaceNon-White
## smokeHxFormer *
## smokeHxAlways .
## smokeHxUnknown
## smokeHxEver
## disadv
## site02R *
## site03R
## site04R
## site05R ***
## site06R
## site07R *
## site09R **
## site1 .
## site101
## site102 .
## site103
## site104 **
## site105
## site106 **
## site107
## site108
## site10R **
## site11R
## site12R **
## site13R *
## site14R
## site15R
## site16R .
## site17R
## site18R *
## site19R .
## site20R ***
## site21R ***
## site22R ***
## site23R ***
## site24R
## site25R
## site26R ***
## site27R .
## site28R
## site29R ***
## site30R .
## site31R *
## site32R **
## site33R ***
## site34R *
## site35R ***
## site36R ***
## site37R .
## site38R ***
## site39R *
## site40R
## site41R ***
## site42R ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SS_IQR 0.9995 1.0005 0.9262 1.0787
## dx_yr 1.2405 0.8061 0.9121 1.6873
## age_dx 1.0100 0.9901 1.0023 1.0178
## sexF 0.8679 1.1522 0.7703 0.9779
## dich_RaceNon-White 1.0051 0.9949 0.9535 1.0596
## smokeHxFormer 1.1112 0.8999 1.0007 1.2340
## smokeHxAlways 1.0493 0.9530 0.9940 1.1077
## smokeHxUnknown 1.0633 0.9405 0.9863 1.1464
## smokeHxEver 0.9933 1.0068 0.9680 1.0192
## disadv 1.0049 0.9951 0.9244 1.0924
## site02R 1.5888 0.6294 1.0674 2.3650
## site03R 0.8151 1.2268 0.6370 1.0431
## site04R 0.8693 1.1504 0.7186 1.0516
## site05R 0.8546 1.1701 0.8395 0.8699
## site06R 1.0659 0.9382 0.8929 1.2723
## site07R 0.6332 1.5794 0.4374 0.9164
## site09R 0.8898 1.1239 0.8192 0.9664
## site1 0.8396 1.1910 0.6933 1.0168
## site101 0.6800 1.4705 0.4222 1.0954
## site102 0.6132 1.6307 0.3749 1.0031
## site103 0.8215 1.2172 0.6482 1.0412
## site104 0.6730 1.4860 0.5161 0.8776
## site105 0.6912 1.4467 0.4316 1.1071
## site106 0.6266 1.5959 0.4506 0.8713
## site107 0.9467 1.0563 0.5490 1.6324
## site108 0.8222 1.2163 0.3308 2.0436
## site10R 0.7452 1.3420 0.6218 0.8930
## site11R 0.8707 1.1485 0.7272 1.0425
## site12R 0.8597 1.1632 0.7784 0.9495
## site13R 0.6330 1.5797 0.4390 0.9128
## site14R 1.1403 0.8770 0.7870 1.6521
## site15R 0.9635 1.0379 0.9200 1.0091
## site16R 1.1837 0.8448 0.9771 1.4339
## site17R 0.9783 1.0222 0.8606 1.1122
## site18R 0.6219 1.6079 0.4098 0.9437
## site19R 0.8798 1.1366 0.7665 1.0100
## site20R 0.7884 1.2684 0.7025 0.8847
## site21R 0.7239 1.3814 0.6600 0.7940
## site22R 0.9165 1.0912 0.8872 0.9466
## site23R 0.8395 1.1912 0.8019 0.8788
## site24R 1.0492 0.9531 0.9573 1.1498
## site25R 0.8725 1.1461 0.5813 1.3098
## site26R 0.6661 1.5014 0.6139 0.7226
## site27R 0.8680 1.1521 0.7488 1.0062
## site28R 1.0226 0.9779 0.8603 1.2156
## site29R 0.6954 1.4381 0.6695 0.7222
## site30R 0.7201 1.3888 0.5111 1.0145
## site31R 0.8272 1.2089 0.6905 0.9908
## site32R 0.6588 1.5179 0.5101 0.8509
## site33R 0.7203 1.3883 0.6825 0.7602
## site34R 0.7736 1.2927 0.6129 0.9763
## site35R 0.8823 1.1335 0.8512 0.9144
## site36R 0.8049 1.2424 0.7249 0.8938
## site37R 0.7527 1.3285 0.5580 1.0154
## site38R 0.7397 1.3518 0.6485 0.8438
## site39R 0.7323 1.3656 0.5766 0.9300
## site40R 0.9936 1.0064 0.7811 1.2640
## site41R 0.7629 1.3107 0.6753 0.8619
## site42R 1.1469 0.8719 1.1082 1.1870
##
## Concordance= 0.676 (se = 0.064 )
## Likelihood ratio test= 2535 on 59 df, p=<2e-16
## Wald test = 137.4 on 59 df, p=3e-08
## Score (logrank) test = 2074 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
So this indicates that there is a HR of 1.00 per IQR increase in SS as compared with a HR of 1.00 per 1ug/m3 increase in SS.
Base model
#First need to make dataframe that only includes patients with a value for event
Allx <- All %>% filter(!is.na(SS) & !is.na(deadORtx) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(cohort) & !is.na(site) & SS>0)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$SS, df=3) + Allx$dx_yr + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$SS, exp(predicted$fit), type="n")
lines(sm.spline(Allx$SS, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$SS, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$SS, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
Complete model
#First need to make dataframe that only includes patients with time_DeathTxCensor
Allx <- All %>% filter(!is.na(SS) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(deadORtx) & !is.na(age_dx) & !is.na(sex) & !is.na(smokeHx) & !is.na(dich_Race) & !is.na(disadv) & !is.na(site) & SS>0.01 & SS<2.5)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$SS, df=3) + Allx$dx_yr + Allx$age_dx + Allx$sex + Allx$smokeHx + Allx$dich_Race + Allx$disadv + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$SS, exp(predicted$fit), type="n")
lines(sm.spline(Allx$SS, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$SS, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$SS, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ Soil + dx_yr + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ Soil + dx_yr +
## site, data = All, id = ID, cluster = cohort)
##
## n= 335367, number of events= 6459
## (1981 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## Soil 0.162021 1.175885 0.090275 0.142038 1.141 0.254001
## dx_yr 0.219065 1.244912 0.005434 0.152456 1.437 0.150746
## site02R 0.449024 1.566782 0.254384 0.166042 2.704 0.006845 **
## site03R -0.318254 0.727418 0.269226 0.157099 -2.026 0.042783 *
## site04R -0.234561 0.790918 0.271865 0.110504 -2.123 0.033784 *
## site05R -0.230171 0.794398 0.276343 0.042655 -5.396 6.81e-08 ***
## site06R 0.049520 1.050767 0.267875 0.094601 0.523 0.600649
## site07R -0.470435 0.624730 0.247021 0.217856 -2.159 0.030820 *
## site09R -0.071298 0.931184 0.269248 0.081035 -0.880 0.378942
## site1 -0.149861 0.860828 0.216341 0.106373 -1.409 0.158886
## site101 -0.399850 0.670421 0.220742 0.259600 -1.540 0.123498
## site102 -0.465011 0.628128 0.220193 0.213163 -2.181 0.029148 *
## site103 -0.158763 0.853199 0.215515 0.149930 -1.059 0.289638
## site104 -0.338132 0.713102 0.221277 0.165751 -2.040 0.041351 *
## site105 -0.333346 0.716522 0.217481 0.253939 -1.313 0.189284
## site106 -0.415554 0.659974 0.220205 0.221803 -1.874 0.060996 .
## site107 -0.008599 0.991438 0.240780 0.293832 -0.029 0.976654
## site108 -0.152102 0.858901 0.240485 0.437164 -0.348 0.727893
## site10R -0.366689 0.693025 0.296976 0.217642 -1.685 0.092022 .
## site11R -0.160036 0.852113 0.242792 0.144944 -1.104 0.269540
## site12R -0.138565 0.870606 0.252615 0.031196 -4.442 8.92e-06 ***
## site13R -0.396941 0.672373 0.240651 0.189769 -2.092 0.036465 *
## site14R 0.112572 1.119153 0.395754 0.170823 0.659 0.509897
## site15R -0.015617 0.984504 0.271588 0.085044 -0.184 0.854302
## site16R 0.125776 1.134029 0.263387 0.055685 2.259 0.023902 *
## site17R -0.130192 0.877927 0.277471 0.072117 -1.805 0.071029 .
## site18R -0.534479 0.585975 0.264932 0.331554 -1.612 0.106953
## site19R -0.280423 0.755464 0.302758 0.211408 -1.326 0.184690
## site20R -0.292978 0.746038 0.289887 0.075641 -3.873 0.000107 ***
## site21R -0.350298 0.704478 0.254919 0.089044 -3.934 8.35e-05 ***
## site22R -0.055063 0.946425 0.247326 0.062793 -0.877 0.380542
## site23R -0.295273 0.744328 0.260787 0.150088 -1.967 0.049145 *
## site24R -0.003148 0.996857 0.249047 0.075521 -0.042 0.966754
## site25R -0.152284 0.858744 0.251272 0.133641 -1.140 0.254492
## site26R -0.515656 0.597109 0.280025 0.237707 -2.169 0.030060 *
## site27R -0.166537 0.846592 0.369323 0.073851 -2.255 0.024131 *
## site28R -0.087355 0.916352 0.266191 0.072301 -1.208 0.226969
## site29R -0.336509 0.714260 0.334886 0.115652 -2.910 0.003618 **
## site30R -0.338999 0.712483 0.257223 0.209088 -1.621 0.104948
## site31R -0.202786 0.816453 0.270668 0.061086 -3.320 0.000901 ***
## site32R -0.693415 0.499866 0.308038 0.328561 -2.110 0.034819 *
## site33R -0.450384 0.637383 0.272457 0.198453 -2.269 0.023240 *
## site34R -0.224777 0.798694 0.247718 0.110804 -2.029 0.042499 *
## site35R -0.061659 0.940203 0.264384 0.048986 -1.259 0.208137
## site36R -0.264599 0.767514 0.255582 0.070835 -3.735 0.000187 ***
## site37R -0.256630 0.773655 0.256783 0.151622 -1.693 0.090538 .
## site38R -0.300128 0.740723 0.261358 0.070549 -4.254 2.10e-05 ***
## site39R -0.351213 0.703834 0.293961 0.149639 -2.347 0.018922 *
## site40R -0.088977 0.914867 0.275196 0.305118 -0.292 0.770581
## site41R -0.296620 0.743327 0.252285 0.118843 -2.496 0.012564 *
## site42R 0.119857 1.127336 0.275377 0.018090 6.626 3.46e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Soil 1.1759 0.8504 0.8901 1.5533
## dx_yr 1.2449 0.8033 0.9234 1.6785
## site02R 1.5668 0.6383 1.1315 2.1694
## site03R 0.7274 1.3747 0.5346 0.9897
## site04R 0.7909 1.2644 0.6369 0.9822
## site05R 0.7944 1.2588 0.7307 0.8637
## site06R 1.0508 0.9517 0.8729 1.2648
## site07R 0.6247 1.6007 0.4076 0.9575
## site09R 0.9312 1.0739 0.7944 1.0915
## site1 0.8608 1.1617 0.6988 1.0604
## site101 0.6704 1.4916 0.4031 1.1151
## site102 0.6281 1.5920 0.4136 0.9539
## site103 0.8532 1.1721 0.6360 1.1446
## site104 0.7131 1.4023 0.5153 0.9868
## site105 0.7165 1.3956 0.4356 1.1786
## site106 0.6600 1.5152 0.4273 1.0194
## site107 0.9914 1.0086 0.5574 1.7635
## site108 0.8589 1.1643 0.3646 2.0233
## site10R 0.6930 1.4429 0.4524 1.0617
## site11R 0.8521 1.1736 0.6414 1.1321
## site12R 0.8706 1.1486 0.8190 0.9255
## site13R 0.6724 1.4873 0.4635 0.9753
## site14R 1.1192 0.8935 0.8007 1.5642
## site15R 0.9845 1.0157 0.8334 1.1631
## site16R 1.1340 0.8818 1.0168 1.2648
## site17R 0.8779 1.1390 0.7622 1.0112
## site18R 0.5860 1.7066 0.3060 1.1223
## site19R 0.7555 1.3237 0.4992 1.1433
## site20R 0.7460 1.3404 0.6432 0.8653
## site21R 0.7045 1.4195 0.5917 0.8388
## site22R 0.9464 1.0566 0.8368 1.0704
## site23R 0.7443 1.3435 0.5546 0.9989
## site24R 0.9969 1.0032 0.8597 1.1559
## site25R 0.8587 1.1645 0.6609 1.1159
## site26R 0.5971 1.6747 0.3747 0.9515
## site27R 0.8466 1.1812 0.7325 0.9784
## site28R 0.9164 1.0913 0.7953 1.0559
## site29R 0.7143 1.4001 0.5694 0.8960
## site30R 0.7125 1.4035 0.4729 1.0734
## site31R 0.8165 1.2248 0.7243 0.9203
## site32R 0.4999 2.0005 0.2625 0.9518
## site33R 0.6374 1.5689 0.4320 0.9404
## site34R 0.7987 1.2520 0.6428 0.9924
## site35R 0.9402 1.0636 0.8541 1.0349
## site36R 0.7675 1.3029 0.6680 0.8818
## site37R 0.7737 1.2926 0.5748 1.0414
## site38R 0.7407 1.3500 0.6451 0.8506
## site39R 0.7038 1.4208 0.5249 0.9437
## site40R 0.9149 1.0931 0.5031 1.6637
## site41R 0.7433 1.3453 0.5889 0.9383
## site42R 1.1273 0.8870 1.0881 1.1680
##
## Concordance= 0.673 (se = 0.067 )
## Likelihood ratio test= 2371 on 51 df, p=<2e-16
## Wald test = 2.33 on 51 df, p=1
## Score (logrank) test = 1914 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ Soil + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ Soil + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All,
## id = ID, cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## Soil 0.189992 1.209240 0.091974 0.128283 1.481 0.138595
## dx_yr 0.215875 1.240947 0.005595 0.155559 1.388 0.165219
## age_dx 0.009946 1.009996 0.001177 0.003952 2.517 0.011852 *
## sexF -0.140764 0.868694 0.026685 0.059926 -2.349 0.018826 *
## dich_RaceNon-White 0.000342 1.000342 0.036806 0.031263 0.011 0.991272
## smokeHxFormer 0.105491 1.111257 0.032871 0.054122 1.949 0.051279 .
## smokeHxAlways 0.046502 1.047600 0.074818 0.023691 1.963 0.049669 *
## smokeHxUnknown 0.061163 1.063072 0.070571 0.038529 1.587 0.112412
## smokeHxEver -0.005830 0.994187 0.049908 0.012666 -0.460 0.645336
## disadv -0.005678 0.994338 0.045691 0.050672 -0.112 0.910782
## site02R 0.425809 1.530828 0.258835 0.161365 2.639 0.008320 **
## site03R -0.323553 0.723574 0.273919 0.145616 -2.222 0.026286 *
## site04R -0.196804 0.821351 0.276221 0.112149 -1.755 0.079286 .
## site05R -0.207465 0.812641 0.282408 0.030762 -6.744 1.54e-11 ***
## site06R 0.012329 1.012406 0.273721 0.121759 0.101 0.919343
## site07R -0.517441 0.596044 0.251677 0.207570 -2.493 0.012672 *
## site09R -0.118962 0.887842 0.275527 0.064253 -1.851 0.064104 .
## site1 -0.214379 0.807042 0.223820 0.081312 -2.637 0.008377 **
## site101 -0.419047 0.657673 0.227591 0.234302 -1.788 0.073697 .
## site102 -0.507681 0.601890 0.227356 0.190181 -2.669 0.007597 **
## site103 -0.188759 0.827986 0.222695 0.114958 -1.642 0.100594
## site104 -0.392000 0.675704 0.228220 0.125617 -3.121 0.001805 **
## site105 -0.384221 0.680981 0.224898 0.223539 -1.719 0.085649 .
## site106 -0.523245 0.592595 0.227437 0.180959 -2.892 0.003834 **
## site107 -0.068284 0.933995 0.247382 0.261610 -0.261 0.794080
## site108 -0.220487 0.802128 0.246807 0.402155 -0.548 0.583511
## site10R -0.435042 0.647237 0.303988 0.206323 -2.109 0.034983 *
## site11R -0.235570 0.790120 0.249244 0.131754 -1.788 0.073784 .
## site12R -0.180155 0.835141 0.257266 0.028125 -6.405 1.50e-10 ***
## site13R -0.477612 0.620263 0.245339 0.180840 -2.641 0.008264 **
## site14R 0.123004 1.130888 0.398649 0.173919 0.707 0.479413
## site15R -0.085151 0.918374 0.276178 0.078174 -1.089 0.276042
## site16R 0.145402 1.156505 0.267562 0.052338 2.778 0.005467 **
## site17R -0.094852 0.909508 0.281737 0.051023 -1.859 0.063027 .
## site18R -0.587680 0.555615 0.269543 0.313891 -1.872 0.061173 .
## site19R -0.357769 0.699234 0.308076 0.191777 -1.866 0.062104 .
## site20R -0.321823 0.724826 0.294265 0.061409 -5.241 1.60e-07 ***
## site21R -0.399744 0.670492 0.259446 0.080336 -4.976 6.50e-07 ***
## site22R -0.109729 0.896077 0.252068 0.056668 -1.936 0.052827 .
## site23R -0.342496 0.709996 0.267545 0.126374 -2.710 0.006725 **
## site24R 0.020056 1.020259 0.253908 0.073195 0.274 0.784077
## site25R -0.183472 0.832376 0.257508 0.133436 -1.375 0.169139
## site26R -0.592786 0.552785 0.284480 0.225334 -2.631 0.008521 **
## site27R -0.151609 0.859324 0.372391 0.071394 -2.124 0.033708 *
## site28R -0.017576 0.982578 0.270680 0.060223 -0.292 0.770410
## site29R -0.373397 0.688392 0.338953 0.120703 -3.094 0.001978 **
## site30R -0.398005 0.671659 0.262414 0.201572 -1.975 0.048324 *
## site31R -0.200675 0.818179 0.274812 0.058286 -3.443 0.000575 ***
## site32R -0.736278 0.478893 0.318833 0.298080 -2.470 0.013509 *
## site33R -0.513666 0.598298 0.277777 0.173827 -2.955 0.003126 **
## site34R -0.267274 0.765463 0.252866 0.103909 -2.572 0.010105 *
## site35R -0.113942 0.892310 0.270178 0.030563 -3.728 0.000193 ***
## site36R -0.269182 0.764005 0.260131 0.056030 -4.804 1.55e-06 ***
## site37R -0.299866 0.740918 0.261221 0.146134 -2.052 0.040170 *
## site38R -0.325017 0.722515 0.266878 0.065056 -4.996 5.86e-07 ***
## site39R -0.417580 0.658639 0.298395 0.132631 -3.148 0.001641 **
## site40R -0.157565 0.854221 0.280690 0.285008 -0.553 0.580369
## site41R -0.309606 0.733736 0.256683 0.113596 -2.725 0.006421 **
## site42R 0.118096 1.125352 0.279434 0.012698 9.301 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Soil 1.2092 0.8270 0.9404 1.5549
## dx_yr 1.2409 0.8058 0.9148 1.6833
## age_dx 1.0100 0.9901 1.0022 1.0178
## sexF 0.8687 1.1512 0.7724 0.9770
## dich_RaceNon-White 1.0003 0.9997 0.9409 1.0636
## smokeHxFormer 1.1113 0.8999 0.9994 1.2356
## smokeHxAlways 1.0476 0.9546 1.0001 1.0974
## smokeHxUnknown 1.0631 0.9407 0.9857 1.1465
## smokeHxEver 0.9942 1.0058 0.9698 1.0192
## disadv 0.9943 1.0057 0.9003 1.0982
## site02R 1.5308 0.6532 1.1158 2.1003
## site03R 0.7236 1.3820 0.5439 0.9626
## site04R 0.8214 1.2175 0.6593 1.0233
## site05R 0.8126 1.2306 0.7651 0.8631
## site06R 1.0124 0.9877 0.7975 1.2853
## site07R 0.5960 1.6777 0.3968 0.8953
## site09R 0.8878 1.1263 0.7828 1.0070
## site1 0.8070 1.2391 0.6882 0.9465
## site101 0.6577 1.5205 0.4155 1.0410
## site102 0.6019 1.6614 0.4146 0.8738
## site103 0.8280 1.2077 0.6610 1.0372
## site104 0.6757 1.4799 0.5282 0.8643
## site105 0.6810 1.4685 0.4394 1.0554
## site106 0.5926 1.6875 0.4156 0.8449
## site107 0.9340 1.0707 0.5593 1.5597
## site108 0.8021 1.2467 0.3647 1.7642
## site10R 0.6472 1.5450 0.4320 0.9698
## site11R 0.7901 1.2656 0.6103 1.0229
## site12R 0.8351 1.1974 0.7903 0.8825
## site13R 0.6203 1.6122 0.4352 0.8841
## site14R 1.1309 0.8843 0.8042 1.5902
## site15R 0.9184 1.0889 0.7879 1.0704
## site16R 1.1565 0.8647 1.0438 1.2814
## site17R 0.9095 1.0995 0.8230 1.0052
## site18R 0.5556 1.7998 0.3003 1.0279
## site19R 0.6992 1.4301 0.4802 1.0183
## site20R 0.7248 1.3796 0.6426 0.8175
## site21R 0.6705 1.4914 0.5728 0.7848
## site22R 0.8961 1.1160 0.8019 1.0013
## site23R 0.7100 1.4085 0.5542 0.9095
## site24R 1.0203 0.9801 0.8839 1.1776
## site25R 0.8324 1.2014 0.6408 1.0812
## site26R 0.5528 1.8090 0.3554 0.8597
## site27R 0.8593 1.1637 0.7471 0.9884
## site28R 0.9826 1.0177 0.8732 1.1057
## site29R 0.6884 1.4527 0.5434 0.8721
## site30R 0.6717 1.4889 0.4524 0.9971
## site31R 0.8182 1.2222 0.7299 0.9172
## site32R 0.4789 2.0881 0.2670 0.8589
## site33R 0.5983 1.6714 0.4256 0.8412
## site34R 0.7655 1.3064 0.6244 0.9384
## site35R 0.8923 1.1207 0.8404 0.9474
## site36R 0.7640 1.3089 0.6845 0.8527
## site37R 0.7409 1.3497 0.5564 0.9866
## site38R 0.7225 1.3841 0.6360 0.8208
## site39R 0.6586 1.5183 0.5079 0.8542
## site40R 0.8542 1.1707 0.4886 1.4934
## site41R 0.7337 1.3629 0.5873 0.9167
## site42R 1.1254 0.8886 1.0977 1.1537
##
## Concordance= 0.676 (se = 0.064 )
## Likelihood ratio test= 2540 on 59 df, p=<2e-16
## Wald test = 2.63 on 59 df, p=1
## Score (logrank) test = 2075 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
summary(All$Soil)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0117 0.2843 0.4002 0.4437 0.5253 2.8544 688
IQR(All$Soil, na.rm=T)
## [1] 0.2410509
# Will use the 5yr pre-censoring IQR (0.2614493), not this one
All <- All %>% mutate(Soil_IQR = Soil/0.2614493)
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ Soil_IQR + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ Soil_IQR + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All,
## id = ID, cluster = cohort)
##
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## Soil_IQR 0.049673 1.050928 0.024046 0.033539 1.481 0.138595
## dx_yr 0.215875 1.240947 0.005595 0.155559 1.388 0.165219
## age_dx 0.009946 1.009996 0.001177 0.003952 2.517 0.011852 *
## sexF -0.140764 0.868694 0.026685 0.059926 -2.349 0.018826 *
## dich_RaceNon-White 0.000342 1.000342 0.036806 0.031263 0.011 0.991272
## smokeHxFormer 0.105491 1.111257 0.032871 0.054122 1.949 0.051279 .
## smokeHxAlways 0.046502 1.047600 0.074818 0.023691 1.963 0.049669 *
## smokeHxUnknown 0.061163 1.063072 0.070571 0.038529 1.587 0.112412
## smokeHxEver -0.005830 0.994187 0.049908 0.012666 -0.460 0.645336
## disadv -0.005678 0.994338 0.045691 0.050672 -0.112 0.910782
## site02R 0.425809 1.530828 0.258835 0.161365 2.639 0.008320 **
## site03R -0.323553 0.723574 0.273919 0.145616 -2.222 0.026286 *
## site04R -0.196804 0.821351 0.276221 0.112149 -1.755 0.079286 .
## site05R -0.207465 0.812641 0.282408 0.030762 -6.744 1.54e-11 ***
## site06R 0.012329 1.012406 0.273721 0.121759 0.101 0.919343
## site07R -0.517441 0.596044 0.251677 0.207570 -2.493 0.012672 *
## site09R -0.118962 0.887842 0.275527 0.064253 -1.851 0.064104 .
## site1 -0.214379 0.807042 0.223820 0.081312 -2.637 0.008377 **
## site101 -0.419047 0.657673 0.227591 0.234302 -1.788 0.073697 .
## site102 -0.507681 0.601890 0.227356 0.190181 -2.669 0.007597 **
## site103 -0.188759 0.827986 0.222695 0.114958 -1.642 0.100594
## site104 -0.392000 0.675704 0.228220 0.125617 -3.121 0.001805 **
## site105 -0.384221 0.680981 0.224898 0.223539 -1.719 0.085649 .
## site106 -0.523245 0.592595 0.227437 0.180959 -2.892 0.003834 **
## site107 -0.068284 0.933995 0.247382 0.261610 -0.261 0.794080
## site108 -0.220487 0.802128 0.246807 0.402155 -0.548 0.583511
## site10R -0.435042 0.647237 0.303988 0.206323 -2.109 0.034983 *
## site11R -0.235570 0.790120 0.249244 0.131754 -1.788 0.073784 .
## site12R -0.180155 0.835141 0.257266 0.028125 -6.405 1.50e-10 ***
## site13R -0.477612 0.620263 0.245339 0.180840 -2.641 0.008264 **
## site14R 0.123004 1.130888 0.398649 0.173919 0.707 0.479413
## site15R -0.085151 0.918374 0.276178 0.078174 -1.089 0.276042
## site16R 0.145402 1.156505 0.267562 0.052338 2.778 0.005467 **
## site17R -0.094852 0.909508 0.281737 0.051023 -1.859 0.063027 .
## site18R -0.587680 0.555615 0.269543 0.313891 -1.872 0.061173 .
## site19R -0.357769 0.699234 0.308076 0.191777 -1.866 0.062104 .
## site20R -0.321823 0.724826 0.294265 0.061409 -5.241 1.60e-07 ***
## site21R -0.399744 0.670492 0.259446 0.080336 -4.976 6.50e-07 ***
## site22R -0.109729 0.896077 0.252068 0.056668 -1.936 0.052827 .
## site23R -0.342496 0.709996 0.267545 0.126374 -2.710 0.006725 **
## site24R 0.020056 1.020259 0.253908 0.073195 0.274 0.784077
## site25R -0.183472 0.832376 0.257508 0.133436 -1.375 0.169139
## site26R -0.592786 0.552785 0.284480 0.225334 -2.631 0.008521 **
## site27R -0.151609 0.859324 0.372391 0.071394 -2.124 0.033708 *
## site28R -0.017576 0.982578 0.270680 0.060223 -0.292 0.770410
## site29R -0.373397 0.688392 0.338953 0.120703 -3.094 0.001978 **
## site30R -0.398005 0.671659 0.262414 0.201572 -1.975 0.048324 *
## site31R -0.200675 0.818179 0.274812 0.058286 -3.443 0.000575 ***
## site32R -0.736278 0.478893 0.318833 0.298080 -2.470 0.013509 *
## site33R -0.513666 0.598298 0.277777 0.173827 -2.955 0.003126 **
## site34R -0.267274 0.765463 0.252866 0.103909 -2.572 0.010105 *
## site35R -0.113942 0.892310 0.270178 0.030563 -3.728 0.000193 ***
## site36R -0.269182 0.764005 0.260131 0.056030 -4.804 1.55e-06 ***
## site37R -0.299866 0.740918 0.261221 0.146134 -2.052 0.040170 *
## site38R -0.325017 0.722515 0.266878 0.065056 -4.996 5.86e-07 ***
## site39R -0.417580 0.658639 0.298395 0.132631 -3.148 0.001641 **
## site40R -0.157565 0.854221 0.280690 0.285008 -0.553 0.580369
## site41R -0.309606 0.733736 0.256683 0.113596 -2.725 0.006421 **
## site42R 0.118096 1.125352 0.279434 0.012698 9.301 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Soil_IQR 1.0509 0.9515 0.9841 1.1223
## dx_yr 1.2409 0.8058 0.9148 1.6833
## age_dx 1.0100 0.9901 1.0022 1.0178
## sexF 0.8687 1.1512 0.7724 0.9770
## dich_RaceNon-White 1.0003 0.9997 0.9409 1.0636
## smokeHxFormer 1.1113 0.8999 0.9994 1.2356
## smokeHxAlways 1.0476 0.9546 1.0001 1.0974
## smokeHxUnknown 1.0631 0.9407 0.9857 1.1465
## smokeHxEver 0.9942 1.0058 0.9698 1.0192
## disadv 0.9943 1.0057 0.9003 1.0982
## site02R 1.5308 0.6532 1.1158 2.1003
## site03R 0.7236 1.3820 0.5439 0.9626
## site04R 0.8214 1.2175 0.6593 1.0233
## site05R 0.8126 1.2306 0.7651 0.8631
## site06R 1.0124 0.9877 0.7975 1.2853
## site07R 0.5960 1.6777 0.3968 0.8953
## site09R 0.8878 1.1263 0.7828 1.0070
## site1 0.8070 1.2391 0.6882 0.9465
## site101 0.6577 1.5205 0.4155 1.0410
## site102 0.6019 1.6614 0.4146 0.8738
## site103 0.8280 1.2077 0.6610 1.0372
## site104 0.6757 1.4799 0.5282 0.8643
## site105 0.6810 1.4685 0.4394 1.0554
## site106 0.5926 1.6875 0.4156 0.8449
## site107 0.9340 1.0707 0.5593 1.5597
## site108 0.8021 1.2467 0.3647 1.7642
## site10R 0.6472 1.5450 0.4320 0.9698
## site11R 0.7901 1.2656 0.6103 1.0229
## site12R 0.8351 1.1974 0.7903 0.8825
## site13R 0.6203 1.6122 0.4352 0.8841
## site14R 1.1309 0.8843 0.8042 1.5902
## site15R 0.9184 1.0889 0.7879 1.0704
## site16R 1.1565 0.8647 1.0438 1.2814
## site17R 0.9095 1.0995 0.8230 1.0052
## site18R 0.5556 1.7998 0.3003 1.0279
## site19R 0.6992 1.4301 0.4802 1.0183
## site20R 0.7248 1.3796 0.6426 0.8175
## site21R 0.6705 1.4914 0.5728 0.7848
## site22R 0.8961 1.1160 0.8019 1.0013
## site23R 0.7100 1.4085 0.5542 0.9095
## site24R 1.0203 0.9801 0.8839 1.1776
## site25R 0.8324 1.2014 0.6408 1.0812
## site26R 0.5528 1.8090 0.3554 0.8597
## site27R 0.8593 1.1637 0.7471 0.9884
## site28R 0.9826 1.0177 0.8732 1.1057
## site29R 0.6884 1.4527 0.5434 0.8721
## site30R 0.6717 1.4889 0.4524 0.9971
## site31R 0.8182 1.2222 0.7299 0.9172
## site32R 0.4789 2.0881 0.2670 0.8589
## site33R 0.5983 1.6714 0.4256 0.8412
## site34R 0.7655 1.3064 0.6244 0.9384
## site35R 0.8923 1.1207 0.8404 0.9474
## site36R 0.7640 1.3089 0.6845 0.8527
## site37R 0.7409 1.3497 0.5564 0.9866
## site38R 0.7225 1.3841 0.6360 0.8208
## site39R 0.6586 1.5183 0.5079 0.8542
## site40R 0.8542 1.1707 0.4886 1.4934
## site41R 0.7337 1.3629 0.5873 0.9167
## site42R 1.1254 0.8886 1.0977 1.1537
##
## Concordance= 0.676 (se = 0.064 )
## Likelihood ratio test= 2540 on 59 df, p=<2e-16
## Wald test = 2.63 on 59 df, p=1
## Score (logrank) test = 2075 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
So this indicates that there is a HR of 1.05 per IQR increase in Soil as compared with a HR of 1.21 per 1ug/m3 increase in Soil.
Base model
#First need to make dataframe that only includes patients with a value for event
Allx <- All %>% filter(!is.na(Soil) & !is.na(deadORtx) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(cohort) & !is.na(site) & Soil<20)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$Soil, df=3) + Allx$dx_yr + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$Soil, exp(predicted$fit), type="n")
lines(sm.spline(Allx$Soil, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$Soil, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$Soil, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
Complete model
#First need to make dataframe that only includes patients with time_DeathTxCensor
Allx <- All %>% filter(!is.na(Soil) & !is.na(time_DeathTxCensor) & !is.na(dx_yr) & !is.na(deadORtx) & !is.na(age_dx) & !is.na(sex) & !is.na(smokeHx) & !is.na(dich_Race) & !is.na(disadv) & !is.na(site) & Soil<20)
#Then make survival function
surv1 <- Surv(Allx$start, Allx$end, Allx$event==1)
fit1 <- coxph(surv1 ~ pspline(Allx$Soil, df=3) + Allx$dx_yr + Allx$age_dx + Allx$sex + Allx$smokeHx + Allx$dich_Race + Allx$disadv + cluster(Allx$cohort) + Allx$site)
predicted <- predict(fit1, type="terms", se.fit=T, terms=1)
#Then plot
plot(Allx$Soil, exp(predicted$fit), type="n")
lines(sm.spline(Allx$Soil, exp(predicted$fit)), col = "red" , lty = 1 )
lines(sm.spline(Allx$Soil, exp(predicted$fit + 1.96 * predicted$se)), col = "orange" , lty = 2 )
lines(sm.spline(Allx$Soil, exp(predicted$fit - 1.96 * predicted$se)), col = "orange" , lty = 2 )
Multi-pollutant model without other covariates aside from dx_yr and site
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr, data=Simm)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr, data = Simm)
##
## coef exp(coef) se(coef) z p
## SO4 0.47543 1.60871 0.16919 2.810 0.00495
## NH4 -1.34488 0.26057 0.53322 -2.522 0.01166
## NO3 0.07989 1.08317 0.25204 0.317 0.75127
## BC 1.95006 7.02908 0.40813 4.778 1.77e-06
## OM 0.10051 1.10573 0.10046 1.000 0.31707
## SS 2.27672 9.74468 0.32152 7.081 1.43e-12
## Soil -2.51412 0.08093 0.31892 -7.883 3.19e-15
## dx_yr 0.06206 1.06402 0.01949 3.184 0.00145
##
## Likelihood ratio test=333.5 on 8 df, p=< 2.2e-16
## n= 88140, number of events= 1373
## (1367 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit1 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=Simm[,c(Xnm, 'start', 'end', 'event', 'dx_yr')], q=4)
qc.survfit1
## Scaled effect size (positive direction, sum of positive coefficients = 0.64)
## NH4 SS OM BC
## 0.323 0.293 0.242 0.142
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.441)
## NO3 Soil SO4
## 0.648 0.224 0.129
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 0.198873 0.071873 0.058004 0.33974 2.767 0.005658
#Lastly the HR is reported through the following
exp(qc.survfit1$coef)
## psi1
## 1.220027
exp(qc.survfit1$ci)
## [1] 1.059719 1.404585
So the HR of the overall model is 1.22 (95% CI 1.06-1.40) and that is primarily driven by NH4, SS, OM, and BC
Now to plot the findings
plot(qc.survfit1)
Complete multi-pollutant model + dx_yr
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex + smokeHx + dich_Race + disadv + dx_yr, data=Simm)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex +
## smokeHx + dich_Race + disadv + dx_yr, data = Simm)
##
## coef exp(coef) se(coef) z p
## SO4 0.522906 1.686923 0.178132 2.935 0.00333
## NH4 -1.550522 0.212137 0.566039 -2.739 0.00616
## NO3 0.141085 1.151523 0.267959 0.527 0.59853
## BC 2.047748 7.750424 0.430917 4.752 2.01e-06
## OM 0.129216 1.137936 0.109650 1.178 0.23862
## SS 2.108554 8.236326 0.343357 6.141 8.20e-10
## Soil -2.574076 0.076224 0.329523 -7.812 5.65e-15
## age_dx 0.018155 1.018321 0.002535 7.160 8.04e-13
## sexF -0.333389 0.716491 0.059329 -5.619 1.92e-08
## smokeHxFormer 0.147130 1.158505 0.067921 2.166 0.03030
## smokeHxAlways -0.345321 0.707993 0.177209 -1.949 0.05134
## smokeHxUnknown -0.095368 0.909038 0.082127 -1.161 0.24555
## dich_RaceNon-White 0.027279 1.027654 0.087549 0.312 0.75536
## disadv 0.408380 1.504379 0.097342 4.195 2.72e-05
## dx_yr 0.047453 1.048597 0.020304 2.337 0.01943
##
## Likelihood ratio test=465.4 on 15 df, p=< 2.2e-16
## n= 84895, number of events= 1332
## (4612 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit2 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=Simm[,c(Xnm, 'age_dx', 'sex', 'smokeHx', 'dich_Race', 'disadv', 'start', 'end', 'event', 'dx_yr')], q=4)
qc.survfit2
## Scaled effect size (positive direction, sum of positive coefficients = 0.632)
## NH4 OM SS BC
## 0.298 0.288 0.269 0.145
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.455)
## NO3 Soil SO4
## 0.644 0.236 0.121
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 0.176884 0.073614 0.032602 0.32117 2.4028 0.01627
#Lastly the HR is reported through the following
exp(qc.survfit2$coef)
## psi1
## 1.193492
exp(qc.survfit2$ci)
## [1] 1.033140 1.378733
So the HR of the overall model is 1.19 (95% CI 1.03-1.38) and that is primarily driven by NH4, OM, SS, and BC
Now to plot the findings
plot(qc.survfit2)
Multi-pollutant model without other covariates aside from dx_yr and site
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr + site, data=PFF)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr + site,
## data = PFF)
##
## coef exp(coef) se(coef) z p
## SO4 0.031253 1.031746 0.189895 0.165 0.86928
## NH4 0.927134 2.527256 0.457183 2.028 0.04257
## NO3 -0.499240 0.606992 0.167536 -2.980 0.00288
## BC -0.417934 0.658406 0.368037 -1.136 0.25613
## OM 0.178600 1.195542 0.076881 2.323 0.02018
## SS 0.121441 1.129123 0.123984 0.979 0.32734
## Soil -0.193370 0.824177 0.187490 -1.031 0.30237
## dx_yr 0.145370 1.156467 0.019092 7.614 2.65e-14
## site02R 0.653902 1.923030 0.297720 2.196 0.02807
## site03R 0.067826 1.070179 0.288888 0.235 0.81438
## site04R 0.065264 1.067441 0.332954 0.196 0.84460
## site05R -0.434478 0.647603 0.339364 -1.280 0.20045
## site06R -0.158683 0.853267 0.316983 -0.501 0.61665
## site07R -0.215107 0.806455 0.297833 -0.722 0.47015
## site08R NA NA 0.000000 NA NA
## site09R -0.085011 0.918503 0.287742 -0.295 0.76766
## site10R 0.012452 1.012530 0.361899 0.034 0.97255
## site11R -0.200321 0.818468 0.301064 -0.665 0.50581
## site12R 0.127611 1.136111 0.290929 0.439 0.66093
## site13R -0.328377 0.720091 0.287513 -1.142 0.25340
## site14R -0.062098 0.939791 0.426642 -0.146 0.88428
## site15R -0.095477 0.908939 0.315948 -0.302 0.76251
## site16R 0.074250 1.077076 0.290663 0.255 0.79837
## site17R -0.007258 0.992768 0.329755 -0.022 0.98244
## site18R -0.351325 0.703755 0.315308 -1.114 0.26518
## site19R 0.290196 1.336690 0.376919 0.770 0.44135
## site20R -0.068691 0.933615 0.334863 -0.205 0.83747
## site21R -0.404267 0.667466 0.313879 -1.288 0.19776
## site22R -0.022023 0.978218 0.293081 -0.075 0.94010
## site23R -0.108428 0.897243 0.327640 -0.331 0.74069
## site24R 0.187573 1.206318 0.292015 0.642 0.52065
## site25R 0.113415 1.120097 0.294927 0.385 0.70057
## site26R -0.363457 0.695269 0.365980 -0.993 0.32066
## site27R -0.158854 0.853121 0.403232 -0.394 0.69362
## site28R -0.179976 0.835290 0.333617 -0.539 0.58956
## site29R -0.315595 0.729355 0.359737 -0.877 0.38033
## site30R 0.075660 1.078596 0.325401 0.233 0.81614
## site31R -0.310188 0.733309 0.309185 -1.003 0.31574
## site32R 0.046774 1.047885 0.404984 0.115 0.90805
## site33R -0.257920 0.772657 0.354189 -0.728 0.46649
## site34R -0.233721 0.791583 0.275695 -0.848 0.39658
## site35R -0.171681 0.842248 0.275652 -0.623 0.53341
## site36R -0.357971 0.699093 0.309997 -1.155 0.24819
## site37R -0.222043 0.800881 0.310850 -0.714 0.47504
## site38R -0.398633 0.671237 0.301931 -1.320 0.18674
## site39R 0.089268 1.093374 0.312885 0.285 0.77541
## site40R 0.598770 1.819878 0.332727 1.800 0.07193
## site41R -0.155584 0.855915 0.286248 -0.544 0.58677
## site42R 0.033413 1.033977 0.325191 0.103 0.91816
##
## Likelihood ratio test=180.4 on 48 df, p=< 2.2e-16
## n= 87154, number of events= 1785
## (472 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit1 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=PFF[,c(Xnm, 'start', 'end', 'event', 'dx_yr', 'site')], q=4)
qc.survfit1
## Scaled effect size (positive direction, sum of positive coefficients = 0.169)
## BC NH4 SS
## 0.7680 0.1586 0.0735
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.29)
## NO3 SO4 Soil OM
## 0.44279 0.37566 0.17900 0.00255
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 -0.120975 0.070184 -0.25853 0.016584 -1.7237 0.08477
#Lastly the HR is reported through the following
exp(qc.survfit1$coef)
## psi1
## 0.8860562
exp(qc.survfit1$ci)
## [1] 0.772183 1.016722
So the HR of the overall model is 0.90 (95% CI 0.78-1.03) and the positive direction of effect (i.e. harmful) primarily driven by BC, OM, and SS
Now to plot the findings
plot(qc.survfit1)
Complete multi-pollutant model + dx_yr
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex + smokeHx + dich_Race + disadv + dx_yr + site, data=PFF)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex +
## smokeHx + dich_Race + disadv + dx_yr + site, data = PFF)
##
## coef exp(coef) se(coef) z p
## SO4 0.021920 1.022162 0.191678 0.114 0.90895
## NH4 0.951012 2.588328 0.461999 2.058 0.03954
## NO3 -0.527852 0.589870 0.170432 -3.097 0.00195
## BC -0.438160 0.645222 0.370825 -1.182 0.23737
## OM 0.195806 1.216291 0.078690 2.488 0.01283
## SS 0.121016 1.128643 0.126009 0.960 0.33687
## Soil -0.167273 0.845969 0.189865 -0.881 0.37831
## age_dx 0.001175 1.001176 0.002512 0.468 0.63988
## sexM 0.043783 1.044756 0.052731 0.830 0.40637
## smokeHxEver 0.045716 1.046777 0.050980 0.897 0.36986
## dich_RaceNon-White -0.043220 0.957701 0.082515 -0.524 0.60043
## disadv -0.035087 0.965521 0.091537 -0.383 0.70149
## dx_yr 0.142622 1.153294 0.019498 7.315 2.58e-13
## site02R 0.629176 1.876065 0.302911 2.077 0.03779
## site03R 0.037433 1.038142 0.293576 0.128 0.89854
## site04R 0.079901 1.083180 0.337714 0.237 0.81297
## site05R -0.443830 0.641574 0.347134 -1.279 0.20105
## site06R -0.189483 0.827387 0.325640 -0.582 0.56065
## site07R -0.234973 0.790592 0.303158 -0.775 0.43829
## site08R NA NA 0.000000 NA NA
## site09R -0.128599 0.879327 0.294854 -0.436 0.66273
## site10R -0.016857 0.983284 0.371045 -0.045 0.96376
## site11R -0.277704 0.757521 0.310576 -0.894 0.37124
## site12R 0.117737 1.124949 0.296374 0.397 0.69118
## site13R -0.362419 0.695990 0.292588 -1.239 0.21547
## site14R -0.070755 0.931691 0.429353 -0.165 0.86911
## site15R -0.140608 0.868830 0.323541 -0.435 0.66386
## site16R 0.069303 1.071761 0.294569 0.235 0.81400
## site17R -0.018262 0.981904 0.335722 -0.054 0.95662
## site18R -0.391164 0.676269 0.322482 -1.213 0.22514
## site19R 0.230148 1.258786 0.381643 0.603 0.54648
## site20R -0.101366 0.903602 0.339560 -0.299 0.76530
## site21R -0.444343 0.641245 0.320101 -1.388 0.16510
## site22R -0.043102 0.957814 0.298223 -0.145 0.88508
## site23R -0.154049 0.857230 0.333941 -0.461 0.64458
## site24R 0.177724 1.194496 0.296633 0.599 0.54908
## site25R 0.120417 1.127967 0.302658 0.398 0.69073
## site26R -0.400965 0.669674 0.371986 -1.078 0.28108
## site27R -0.150516 0.860264 0.406888 -0.370 0.71144
## site28R -0.170347 0.843372 0.340805 -0.500 0.61719
## site29R -0.346508 0.707153 0.363679 -0.953 0.34070
## site30R 0.076594 1.079604 0.332195 0.231 0.81765
## site31R -0.323074 0.723920 0.314171 -1.028 0.30379
## site32R -0.011359 0.988705 0.415500 -0.027 0.97819
## site33R -0.302228 0.739170 0.360705 -0.838 0.40210
## site34R -0.258191 0.772448 0.281290 -0.918 0.35868
## site35R -0.219257 0.803115 0.282376 -0.776 0.43747
## site36R -0.371007 0.690039 0.316293 -1.173 0.24080
## site37R -0.232755 0.792348 0.316459 -0.735 0.46204
## site38R -0.427690 0.652014 0.309347 -1.383 0.16680
## site39R 0.047296 1.048433 0.316894 0.149 0.88136
## site40R 0.560805 1.752083 0.338463 1.657 0.09754
## site41R -0.158676 0.853273 0.291568 -0.544 0.58629
## site42R 0.021131 1.021355 0.329707 0.064 0.94890
##
## Likelihood ratio test=181.3 on 53 df, p=6.214e-16
## n= 85935, number of events= 1759
## (1691 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit2 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=PFF[,c(Xnm, 'age_dx', 'sex', 'smokeHx', 'dich_Race', 'disadv', 'start', 'end', 'event', 'dx_yr', 'site')], q=4)
qc.survfit2
## Scaled effect size (positive direction, sum of positive coefficients = 0.168)
## BC NH4 SS OM
## 0.8067 0.0875 0.0547 0.0511
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.294)
## NO3 SO4 Soil
## 0.455 0.366 0.180
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 -0.125920 0.071084 -0.26524 0.013403 -1.7714 0.07649
#Lastly the HR is reported through the following
exp(qc.survfit2$coef)
## psi1
## 0.8816856
exp(qc.survfit2$ci)
## [1] 0.767020 1.013493
So the HR of the overall model is 0.90 (95% CI 0.79-1.03) and the positive direction of effect (i.e. harmful) primarily driven by BC, OM, and SS
Now to plot the findings
plot(qc.survfit2)
Multi-pollutant model without other covariates aside from dx_yr and site
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr + site, data=CARE)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr + site,
## data = CARE)
##
## coef exp(coef) se(coef) z p
## SO4 0.132154 1.141284 0.347663 0.380 0.70386
## NH4 -5.459114 0.004257 0.681654 -8.009 1.16e-15
## NO3 1.771168 5.877715 0.234817 7.543 4.60e-14
## BC 2.543283 12.721368 0.486065 5.232 1.67e-07
## OM -0.716815 0.488305 0.074945 -9.565 < 2e-16
## SS -0.642256 0.526104 0.250671 -2.562 0.01040
## Soil 1.905576 6.723277 0.279244 6.824 8.85e-12
## dx_yr 0.659633 1.934083 0.019572 33.703 < 2e-16
## site102 -0.135506 0.873274 0.152558 -0.888 0.37442
## site103 0.459785 1.583734 0.161234 2.852 0.00435
## site104 0.440262 1.553114 0.173002 2.545 0.01093
## site105 0.611000 1.842273 0.098370 6.211 5.26e-10
## site106 0.190342 1.209663 0.081239 2.343 0.01913
## site107 0.899358 2.458024 0.144935 6.205 5.46e-10
## site108 -0.879056 0.415175 0.159393 -5.515 3.49e-08
##
## Likelihood ratio test=5081 on 15 df, p=< 2.2e-16
## n= 160073, number of events= 3301
## (142 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit1 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=CARE[,c(Xnm, 'start', 'end', 'event', 'dx_yr', 'site')], q=4)
qc.survfit1
## Scaled effect size (positive direction, sum of positive coefficients = 0.375)
## Soil NO3 BC
## 0.386 0.318 0.296
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.647)
## NH4 OM SO4 SS
## 0.4380 0.3815 0.1021 0.0784
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 -0.271654 0.046389 -0.36257 -0.18073 -5.856 4.74e-09
#Lastly the HR is reported through the following
exp(qc.survfit1$coef)
## psi1
## 0.7621178
exp(qc.survfit1$ci)
## [1] 0.6958825 0.8346574
So the HR of the overall model is 0.76 (95% CI 0.70-0.83) with the positive direction of effects (i.e. harmful) primarily driven by Soil, NO3, and BC
Now to plot the findings
plot(qc.survfit1)
Complete multi-pollutant model + dx_yr
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex + smokeHx + dich_Race + disadv + dx_yr + site, data=CARE)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex +
## smokeHx + dich_Race + disadv + dx_yr + site, data = CARE)
##
## coef exp(coef) se(coef) z p
## SO4 0.243661 1.275912 0.352160 0.692 0.48900
## NH4 -5.667471 0.003457 0.685851 -8.263 < 2e-16
## NO3 1.813419 6.131374 0.235893 7.687 1.50e-14
## BC 2.632680 13.911004 0.493520 5.334 9.58e-08
## OM -0.723846 0.484884 0.075697 -9.562 < 2e-16
## SS -0.651174 0.521433 0.257022 -2.534 0.01129
## Soil 1.810289 6.112213 0.281468 6.432 1.26e-10
## age_dx 0.010119 1.010170 0.001615 6.264 3.74e-10
## sexF -0.109652 0.896146 0.036632 -2.993 0.00276
## smokeHxFormer 0.025519 1.025848 0.038756 0.658 0.51024
## smokeHxAlways 0.003077 1.003082 0.083667 0.037 0.97066
## smokeHxUnknown 0.155587 1.168344 0.337416 0.461 0.64472
## dich_RaceNon-White -0.027267 0.973101 0.047657 -0.572 0.56721
## disadv -0.067314 0.934902 0.066452 -1.013 0.31107
## dx_yr 0.655309 1.925737 0.019752 33.176 < 2e-16
## site102 -0.124368 0.883055 0.154813 -0.803 0.42178
## site103 0.479227 1.614826 0.163256 2.935 0.00333
## site104 0.433483 1.542621 0.174896 2.479 0.01319
## site105 0.592685 1.808839 0.099434 5.961 2.51e-09
## site106 0.109507 1.115728 0.082152 1.333 0.18254
## site107 0.866340 2.378190 0.146889 5.898 3.68e-09
## site108 -0.909239 0.402831 0.160938 -5.650 1.61e-08
##
## Likelihood ratio test=5144 on 22 df, p=< 2.2e-16
## n= 160069, number of events= 3300
## (146 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit2 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=CARE[,c(Xnm, 'age_dx', 'sex', 'smokeHx', 'dich_Race', 'disadv', 'start', 'end', 'event', 'dx_yr', 'site')], q=4)
qc.survfit2
## Scaled effect size (positive direction, sum of positive coefficients = 0.378)
## Soil NO3 BC
## 0.390 0.317 0.293
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.647)
## NH4 OM SO4 SS
## 0.4449 0.3746 0.0912 0.0893
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 -0.268395 0.046872 -0.36026 -0.17653 -5.7261 1.027e-08
#Lastly the HR is reported through the following
exp(qc.survfit2$coef)
## psi1
## 0.7646059
exp(qc.survfit2$ci)
## [1] 0.6974935 0.8381757
So the HR of the overall model is 0.76 (95% CI 0.70-0.84) with the positive direction of effects (i.e. harmful) primarily driven by Soil, NO3, and BC
Now to plot the findings
plot(qc.survfit2)
Multi-pollutant model without other covariates aside from dx_yr and site
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr + site + cluster(cohort), data=All)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr + site,
## data = All, cluster = cohort)
##
## coef exp(coef) se(coef) robust se z p
## SO4 1.108729 3.030505 0.086215 0.538426 2.059 0.039475
## NH4 -0.993313 0.370348 0.265729 1.548234 -0.642 0.521147
## NO3 -0.154710 0.856663 0.113661 0.476470 -0.325 0.745408
## BC 3.006893 20.224456 0.189290 2.362709 1.273 0.203144
## OM -0.529659 0.588806 0.038869 0.409164 -1.294 0.195496
## SS -0.063886 0.938112 0.103118 0.379890 -0.168 0.866450
## Soil -0.121733 0.885384 0.132499 0.287495 -0.423 0.671983
## dx_yr 0.350893 1.420336 0.009046 0.148491 2.363 0.018125
## site02R 0.410522 1.507605 0.286724 0.389285 1.055 0.291629
## site03R -0.535539 0.585353 0.275947 0.515505 -1.039 0.298868
## site04R -1.463077 0.231523 0.296217 0.883062 -1.657 0.097555
## site05R -1.894651 0.150371 0.288781 0.786802 -2.408 0.016038
## site06R -1.437573 0.237504 0.276405 0.637345 -2.256 0.024098
## site07R -1.362328 0.256064 0.262883 0.647437 -2.104 0.035362
## site09R -0.966249 0.380508 0.273584 0.414404 -2.332 0.019719
## site1 -2.284759 0.101799 0.230902 1.091159 -2.094 0.036270
## site101 -1.113484 0.328413 0.227134 0.503861 -2.210 0.027112
## site102 -0.374908 0.687353 0.222824 0.176022 -2.130 0.033180
## site103 -0.208375 0.811902 0.215863 0.088923 -2.343 0.019113
## site104 -0.393795 0.674492 0.222376 0.054787 -7.188 6.59e-13
## site105 -0.628453 0.533416 0.220729 0.176534 -3.560 0.000371
## site106 -1.237766 0.290031 0.228386 0.551667 -2.244 0.024853
## site107 -0.347693 0.706316 0.243717 0.181918 -1.911 0.055971
## site108 -0.447822 0.639018 0.245688 0.430183 -1.041 0.297874
## site10R -1.205880 0.299428 0.317410 0.494673 -2.438 0.014780
## site11R -1.683655 0.185694 0.253589 0.780840 -2.156 0.031067
## site12R -0.617754 0.539154 0.267632 0.437462 -1.412 0.157912
## site13R -1.902421 0.149207 0.251546 0.807326 -2.356 0.018451
## site14R -1.313274 0.268938 0.405549 0.643241 -2.042 0.041186
## site15R -1.463415 0.231445 0.279684 0.597869 -2.448 0.014376
## site16R -1.024201 0.359083 0.272177 0.549982 -1.862 0.062569
## site17R -1.315865 0.268242 0.291586 0.717923 -1.833 0.066821
## site18R -1.734137 0.176552 0.272034 0.674880 -2.570 0.010183
## site19R -0.422756 0.655239 0.330864 0.634581 -0.666 0.505286
## site20R -1.136242 0.321023 0.302036 0.602719 -1.885 0.059404
## site21R -2.157402 0.115625 0.268768 0.930645 -2.318 0.020440
## site22R -1.541335 0.214095 0.262533 0.826630 -1.865 0.062237
## site23R -1.265254 0.282168 0.274567 0.665038 -1.903 0.057102
## site24R -0.425133 0.653683 0.266214 0.378451 -1.123 0.261288
## site25R -0.129724 0.878337 0.280325 0.315273 -0.411 0.680730
## site26R -1.732274 0.176882 0.298739 0.695066 -2.492 0.012694
## site27R -1.663569 0.189462 0.376894 0.720590 -2.309 0.020965
## site28R -1.822800 0.161573 0.282450 0.877685 -2.077 0.037818
## site29R -1.537592 0.214898 0.341795 0.517310 -2.972 0.002956
## site30R -1.653168 0.191442 0.286892 1.041288 -1.588 0.112373
## site31R -1.618317 0.198232 0.277169 0.618423 -2.617 0.008875
## site32R -0.854110 0.425662 0.341808 0.881196 -0.969 0.332415
## site33R -1.565045 0.209079 0.289238 0.701808 -2.230 0.025746
## site34R -1.196584 0.302225 0.253687 0.511347 -2.340 0.019280
## site35R -0.784158 0.456504 0.266177 0.249101 -3.148 0.001644
## site36R -1.694074 0.183769 0.268098 0.799006 -2.120 0.033987
## site37R -1.721940 0.178719 0.273122 0.823633 -2.091 0.036558
## site38R -1.667340 0.188749 0.267896 0.584201 -2.854 0.004317
## site39R -0.445251 0.640664 0.304685 0.529867 -0.840 0.400737
## site40R -1.296523 0.273481 0.317782 1.576595 -0.822 0.410874
## site41R -1.543715 0.213586 0.264996 0.786001 -1.964 0.049529
## site42R -1.468058 0.230372 0.287490 0.735193 -1.997 0.045843
##
## Likelihood ratio test=3375 on 57 df, p=< 2.2e-16
## n= 335367, number of events= 6459
## (1981 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit1 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=All[,c(Xnm, 'start', 'end', 'event', 'dx_yr', 'site', 'cohort')], q=4)
qc.survfit1
## Scaled effect size (positive direction, sum of positive coefficients = 0.592)
## BC Soil SO4
## 0.600 0.229 0.172
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.587)
## OM NH4 NO3 SS
## 0.4111 0.3254 0.2333 0.0301
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 0.0048171 0.0374287 -0.068542 0.078176 0.1287 0.8976
#Lastly the HR is reported through the following
exp(qc.survfit1$coef)
## psi1
## 1.004829
exp(qc.survfit1$ci)
## [1] 0.9337545 1.0813129
So the HR of the overall model is 1.02 (95% CI 0.95-1.09) with the positive direction of effects (i.e. harmful) primarily driven by BC, Soil, SO4
Now to plot the findings
plot(qc.survfit1)
Complete multi-pollutant model + dx_yr
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex + smokeHx + dich_Race + disadv + dx_yr + site + cluster(cohort), data=All)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex +
## smokeHx + dich_Race + disadv + dx_yr + site, data = All,
## cluster = cohort)
##
## coef exp(coef) se(coef) robust se z p
## SO4 1.108152 3.028756 0.087559 0.558069 1.986 0.047068
## NH4 -0.977642 0.376197 0.269984 1.614064 -0.606 0.544713
## NO3 -0.145273 0.864787 0.115270 0.509733 -0.285 0.775646
## BC 3.147118 23.268916 0.191483 2.373037 1.326 0.184774
## OM -0.550872 0.576447 0.039446 0.410813 -1.341 0.179944
## SS -0.093092 0.911110 0.104288 0.393518 -0.237 0.812996
## Soil -0.125732 0.881852 0.133200 0.263884 -0.476 0.633743
## age_dx 0.008946 1.008987 0.001176 0.003174 2.819 0.004822
## sexF -0.150299 0.860451 0.026733 0.065005 -2.312 0.020772
## smokeHxFormer 0.105799 1.111599 0.033080 0.038742 2.731 0.006317
## smokeHxAlways -0.011978 0.988094 0.075048 0.043948 -0.273 0.785205
## smokeHxUnknown -0.109979 0.895853 0.070515 0.047446 -2.318 0.020452
## smokeHxEver 0.001228 1.001229 0.049880 0.017999 0.068 0.945603
## dich_RaceNon-White 0.008509 1.008545 0.037070 0.039067 0.218 0.827586
## disadv 0.100696 1.105940 0.046483 0.046936 2.145 0.031923
## dx_yr 0.348953 1.417583 0.009249 0.150440 2.320 0.020365
## site02R 0.414442 1.513526 0.291126 0.375301 1.104 0.269467
## site03R -0.523422 0.592489 0.280146 0.501853 -1.043 0.296958
## site04R -1.459834 0.232275 0.300887 0.911960 -1.601 0.109429
## site05R -1.925254 0.145839 0.294909 0.788842 -2.441 0.014663
## site06R -1.530439 0.216441 0.282460 0.629291 -2.432 0.015015
## site07R -1.436115 0.237850 0.267144 0.655942 -2.189 0.028568
## site09R -1.041338 0.352982 0.279786 0.421492 -2.471 0.013489
## site1 -2.372598 0.093238 0.238308 1.071202 -2.215 0.026767
## site101 -1.150650 0.316431 0.233772 0.482478 -2.385 0.017085
## site102 -0.397530 0.671978 0.229572 0.152994 -2.598 0.009368
## site103 -0.248863 0.779687 0.223122 0.056570 -4.399 1.09e-05
## site104 -0.452037 0.636331 0.229292 0.034147 -13.238 < 2e-16
## site105 -0.675072 0.509120 0.228031 0.151917 -4.444 8.84e-06
## site106 -1.355233 0.257887 0.235201 0.528953 -2.562 0.010404
## site107 -0.398735 0.671169 0.250199 0.164161 -2.429 0.015143
## site108 -0.511230 0.599757 0.251762 0.412234 -1.240 0.214922
## site10R -1.246929 0.287386 0.324246 0.450080 -2.770 0.005598
## site11R -1.785533 0.167708 0.260170 0.768710 -2.323 0.020192
## site12R -0.695944 0.498603 0.271990 0.466269 -1.493 0.135547
## site13R -2.007853 0.134277 0.256179 0.817602 -2.456 0.014058
## site14R -1.331048 0.264200 0.408305 0.642390 -2.072 0.038263
## site15R -1.552995 0.211613 0.284675 0.587252 -2.645 0.008181
## site16R -1.039581 0.353603 0.276552 0.552473 -1.882 0.059878
## site17R -1.331872 0.263983 0.296084 0.725862 -1.835 0.066523
## site18R -1.798532 0.165542 0.276636 0.644237 -2.792 0.005243
## site19R -0.479149 0.619310 0.335010 0.588349 -0.814 0.415418
## site20R -1.183920 0.306076 0.305999 0.611662 -1.936 0.052919
## site21R -2.236702 0.106810 0.273614 0.919152 -2.433 0.014956
## site22R -1.636044 0.194749 0.267436 0.842882 -1.941 0.052257
## site23R -1.315081 0.268453 0.280580 0.630726 -2.085 0.037067
## site24R -0.426788 0.652602 0.270521 0.394885 -1.081 0.279791
## site25R -0.143846 0.866021 0.286185 0.294590 -0.488 0.625343
## site26R -1.794732 0.166172 0.302462 0.644882 -2.783 0.005385
## site27R -1.679963 0.186381 0.379947 0.732014 -2.295 0.021734
## site28R -1.823120 0.161521 0.287391 0.883932 -2.063 0.039159
## site29R -1.561639 0.209792 0.345921 0.524446 -2.978 0.002904
## site30R -1.771261 0.170118 0.292786 1.076019 -1.646 0.099738
## site31R -1.642615 0.193474 0.281385 0.619613 -2.651 0.008025
## site32R -0.880623 0.414525 0.348876 0.810909 -1.086 0.277492
## site33R -1.609281 0.200031 0.293709 0.640906 -2.511 0.012041
## site34R -1.257423 0.284386 0.258531 0.520677 -2.415 0.015736
## site35R -0.845418 0.429378 0.271976 0.248153 -3.407 0.000657
## site36R -1.759497 0.172131 0.272735 0.808631 -2.176 0.029563
## site37R -1.814536 0.162913 0.277593 0.846376 -2.144 0.032042
## site38R -1.724598 0.178245 0.273644 0.582442 -2.961 0.003067
## site39R -0.528256 0.589632 0.309102 0.530223 -0.996 0.319109
## site40R -1.458496 0.232586 0.324919 1.611728 -0.905 0.365504
## site41R -1.608660 0.200156 0.269845 0.803091 -2.003 0.045168
## site42R -1.510156 0.220875 0.291605 0.739732 -2.041 0.041202
##
## Likelihood ratio test=3543 on 65 df, p=< 2.2e-16
## n= 330899, number of events= 6391
## (6449 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit2 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=All[,c(Xnm, 'age_dx', 'sex', 'smokeHx', 'dich_Race', 'disadv', 'start', 'end', 'event', 'dx_yr', 'site', 'cohort')], q=4)
qc.survfit2
## Scaled effect size (positive direction, sum of positive coefficients = 0.6)
## BC Soil SO4
## 0.590 0.212 0.198
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.589)
## OM NH4 NO3 SS
## 0.3921 0.3252 0.2386 0.0441
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 0.011551 0.037929 -0.062789 0.085891 0.3045 0.7607
#Lastly the HR is reported through the following
exp(qc.survfit2$coef)
## psi1
## 1.011618
exp(qc.survfit2$ci)
## [1] 0.9391415 1.0896873
So the HR of the overall model is 1.02 (95% CI 0.95-1.10) with the positive direction of effects (i.e. harmful) primarily driven by BC, SO4, and Soil
Now to plot the findings
plot(qc.survfit2)
Simm_IPF <- Simm %>% filter(dx=="IPF")
PFF_IPF <- PFF %>% filter(dx=="IPF")
CARE_IPF <- CARE %>% filter(dx=="IPF")
All_IPF <- All %>% filter(dx=="IPF")
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ PM + dx_yr, data=Simm_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ PM + dx_yr, data = Simm_IPF,
## id = ID)
##
## n= 31982, number of events= 695
## (446 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## PM 0.02683 1.02719 0.02436 1.101 0.27079
## dx_yr 0.03347 1.03404 0.01249 2.681 0.00734 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## PM 1.027 0.9735 0.9793 1.077
## dx_yr 1.034 0.9671 1.0090 1.060
##
## Concordance= 0.52 (se = 0.013 )
## Likelihood ratio test= 8.81 on 2 df, p=0.01
## Wald test = 8.75 on 2 df, p=0.01
## Score (logrank) test = 8.76 on 2 df, p=0.01
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ PM + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv, data=Simm_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ PM + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv, data = Simm_IPF, id = ID)
##
## n= 30488, number of events= 670
## (1940 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## PM 0.026844 1.027208 0.025293 1.061 0.28854
## dx_yr 0.021340 1.021570 0.013194 1.617 0.10579
## age_dx 0.002010 1.002012 0.004555 0.441 0.65900
## sexF -0.375968 0.686625 0.088853 -4.231 2.32e-05 ***
## dich_RaceNon-White 0.163702 1.177863 0.128527 1.274 0.20278
## smokeHxFormer 0.082335 1.085820 0.091977 0.895 0.37069
## smokeHxAlways -0.402321 0.668766 0.266624 -1.509 0.13131
## smokeHxUnknown 0.495516 1.641345 0.169973 2.915 0.00355 **
## disadv 0.385780 1.470761 0.134234 2.874 0.00405 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## PM 1.0272 0.9735 0.9775 1.0794
## dx_yr 1.0216 0.9789 0.9955 1.0483
## age_dx 1.0020 0.9980 0.9931 1.0110
## sexF 0.6866 1.4564 0.5769 0.8172
## dich_RaceNon-White 1.1779 0.8490 0.9156 1.5153
## smokeHxFormer 1.0858 0.9210 0.9067 1.3003
## smokeHxAlways 0.6688 1.4953 0.3966 1.1278
## smokeHxUnknown 1.6413 0.6093 1.1763 2.2902
## disadv 1.4708 0.6799 1.1305 1.9134
##
## Concordance= 0.591 (se = 0.012 )
## Likelihood ratio test= 52.75 on 9 df, p=3e-08
## Wald test = 53.33 on 9 df, p=3e-08
## Score (logrank) test = 54.06 on 9 df, p=2e-08
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ PM + dx_yr + site, data=PFF_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ PM + dx_yr + site,
## data = PFF_IPF, id = ID)
##
## n= 54499, number of events= 1146
## (217 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## PM 0.011916 1.011987 0.025653 0.464 0.6423
## dx_yr 0.107435 1.113418 0.015228 7.055 1.73e-12 ***
## site02R 0.623805 1.866015 0.344110 1.813 0.0699 .
## site03R -0.242860 0.784381 0.347132 -0.700 0.4842
## site04R -0.249013 0.779570 0.458674 -0.543 0.5872
## site05R -0.349963 0.704714 0.385035 -0.909 0.3634
## site06R -0.052940 0.948437 0.331987 -0.159 0.8733
## site07R -0.333392 0.716489 0.312225 -1.068 0.2856
## site08R NA NA 0.000000 NA NA
## site09R -0.032164 0.968347 0.342388 -0.094 0.9252
## site10R -0.158498 0.853425 0.386348 -0.410 0.6816
## site11R -0.093865 0.910405 0.310083 -0.303 0.7621
## site12R -0.088763 0.915062 0.334528 -0.265 0.7907
## site13R -0.314391 0.730234 0.308481 -1.019 0.3081
## site14R -0.382317 0.682278 0.645221 -0.593 0.5535
## site15R 0.015737 1.015861 0.335894 0.047 0.9626
## site16R 0.117281 1.124435 0.348898 0.336 0.7368
## site17R 0.001297 1.001297 0.371099 0.003 0.9972
## site18R -0.296922 0.743102 0.329973 -0.900 0.3682
## site19R -0.001157 0.998843 0.350358 -0.003 0.9974
## site20R -0.265193 0.767058 0.350537 -0.757 0.4493
## site21R -0.321279 0.725221 0.332269 -0.967 0.3336
## site22R -0.089091 0.914762 0.314649 -0.283 0.7771
## site23R -0.179658 0.835556 0.328299 -0.547 0.5842
## site24R -0.147814 0.862592 0.332239 -0.445 0.6564
## site25R -0.032406 0.968114 0.330662 -0.098 0.9219
## site26R -0.418976 0.657720 0.348948 -1.201 0.2299
## site27R -0.008847 0.991192 0.762873 -0.012 0.9907
## site28R -0.160623 0.851613 0.447259 -0.359 0.7195
## site29R -0.294579 0.744845 0.436056 -0.676 0.4993
## site30R -0.250205 0.778641 0.349491 -0.716 0.4740
## site31R -0.319448 0.726550 0.450007 -0.710 0.4778
## site32R -0.363701 0.695099 0.371617 -0.979 0.3277
## site33R -0.352929 0.702627 0.336332 -1.049 0.2940
## site34R -0.148796 0.861745 0.314529 -0.473 0.6362
## site35R -0.109250 0.896506 0.334037 -0.327 0.7436
## site36R -0.228369 0.795830 0.348053 -0.656 0.5117
## site37R -0.197537 0.820750 0.344484 -0.573 0.5664
## site38R -0.301230 0.739907 0.333182 -0.904 0.3659
## site39R -0.208823 0.811539 0.360762 -0.579 0.5627
## site40R 0.094558 1.099173 0.383902 0.246 0.8054
## site41R -0.226236 0.797530 0.373636 -0.605 0.5448
## site42R 0.048681 1.049886 0.368957 0.132 0.8950
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## PM 1.0120 0.9882 0.9624 1.064
## dx_yr 1.1134 0.8981 1.0807 1.147
## site02R 1.8660 0.5359 0.9506 3.663
## site03R 0.7844 1.2749 0.3972 1.549
## site04R 0.7796 1.2828 0.3173 1.915
## site05R 0.7047 1.4190 0.3313 1.499
## site06R 0.9484 1.0544 0.4948 1.818
## site07R 0.7165 1.3957 0.3885 1.321
## site08R NA NA NA NA
## site09R 0.9683 1.0327 0.4950 1.894
## site10R 0.8534 1.1717 0.4002 1.820
## site11R 0.9104 1.0984 0.4958 1.672
## site12R 0.9151 1.0928 0.4750 1.763
## site13R 0.7302 1.3694 0.3989 1.337
## site14R 0.6823 1.4657 0.1926 2.416
## site15R 1.0159 0.9844 0.5259 1.962
## site16R 1.1244 0.8893 0.5675 2.228
## site17R 1.0013 0.9987 0.4838 2.072
## site18R 0.7431 1.3457 0.3892 1.419
## site19R 0.9988 1.0012 0.5027 1.985
## site20R 0.7671 1.3037 0.3859 1.525
## site21R 0.7252 1.3789 0.3781 1.391
## site22R 0.9148 1.0932 0.4937 1.695
## site23R 0.8356 1.1968 0.4391 1.590
## site24R 0.8626 1.1593 0.4498 1.654
## site25R 0.9681 1.0329 0.5064 1.851
## site26R 0.6577 1.5204 0.3319 1.303
## site27R 0.9912 1.0089 0.2222 4.421
## site28R 0.8516 1.1742 0.3544 2.046
## site29R 0.7448 1.3426 0.3169 1.751
## site30R 0.7786 1.2843 0.3925 1.545
## site31R 0.7266 1.3764 0.3008 1.755
## site32R 0.6951 1.4386 0.3355 1.440
## site33R 0.7026 1.4232 0.3634 1.358
## site34R 0.8617 1.1604 0.4652 1.596
## site35R 0.8965 1.1154 0.4658 1.725
## site36R 0.7958 1.2565 0.4023 1.574
## site37R 0.8207 1.2184 0.4178 1.612
## site38R 0.7399 1.3515 0.3851 1.422
## site39R 0.8115 1.2322 0.4002 1.646
## site40R 1.0992 0.9098 0.5180 2.333
## site41R 0.7975 1.2539 0.3834 1.659
## site42R 1.0499 0.9525 0.5094 2.164
##
## Concordance= 0.59 (se = 0.009 )
## Likelihood ratio test= 110.7 on 42 df, p=4e-08
## Wald test = 109.1 on 42 df, p=7e-08
## Score (logrank) test = 113.4 on 42 df, p=2e-08
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ PM + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=PFF_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ PM + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = PFF_IPF,
## id = ID)
##
## n= 53867, number of events= 1131
## (849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## PM 0.0142839 1.0143864 0.0261075 0.547 0.584
## dx_yr 0.1068047 1.1127170 0.0157189 6.795 1.09e-11 ***
## age_dx -0.0002805 0.9997195 0.0039344 -0.071 0.943
## sexM 0.0611311 1.0630382 0.0713103 0.857 0.391
## dich_RaceNon-White -0.0625902 0.9393284 0.1263612 -0.495 0.620
## smokeHxEver 0.0430378 1.0439774 0.0646553 0.666 0.506
## disadv -0.0517598 0.9495569 0.1120933 -0.462 0.644
## site02R 0.5681626 1.7650210 0.3575818 1.589 0.112
## site03R -0.2710643 0.7625675 0.3591658 -0.755 0.450
## site04R -0.3103185 0.7332134 0.4702289 -0.660 0.509
## site05R -0.4026734 0.6685304 0.3987572 -1.010 0.313
## site06R -0.0940045 0.9102787 0.3484233 -0.270 0.787
## site07R -0.3807946 0.6833182 0.3272209 -1.164 0.245
## site08R NA NA 0.0000000 NA NA
## site09R -0.1151220 0.8912574 0.3573719 -0.322 0.747
## site10R -0.2176135 0.8044363 0.4001451 -0.544 0.587
## site11R -0.1573101 0.8544391 0.3293543 -0.478 0.633
## site12R -0.1341212 0.8744841 0.3486543 -0.385 0.700
## site13R -0.3756688 0.6868297 0.3226027 -1.164 0.244
## site14R -0.4489588 0.6382924 0.6532020 -0.687 0.492
## site15R -0.0369873 0.9636883 0.3523427 -0.105 0.916
## site16R 0.0691054 1.0715492 0.3608654 0.191 0.848
## site17R -0.0625400 0.9393755 0.3857200 -0.162 0.871
## site18R -0.3363485 0.7143741 0.3464050 -0.971 0.332
## site19R -0.0457425 0.9552879 0.3625823 -0.126 0.900
## site20R -0.3202992 0.7259318 0.3650363 -0.877 0.380
## site21R -0.3713135 0.6898276 0.3472106 -1.069 0.285
## site22R -0.1477421 0.8626536 0.3299279 -0.448 0.654
## site23R -0.2480252 0.7803403 0.3449457 -0.719 0.472
## site24R -0.2052051 0.8144802 0.3463544 -0.592 0.554
## site25R -0.0686836 0.9336221 0.3444899 -0.199 0.842
## site26R -0.4564070 0.6335559 0.3626695 -1.258 0.208
## site27R -0.1192351 0.8875991 0.7742839 -0.154 0.878
## site28R -0.2003847 0.8184158 0.4602171 -0.435 0.663
## site29R -0.3512994 0.7037730 0.4479388 -0.784 0.433
## site30R -0.3135246 0.7308664 0.3639645 -0.861 0.389
## site31R -0.3707204 0.6902369 0.4592058 -0.807 0.419
## site32R -0.4300017 0.6505080 0.3952399 -1.088 0.277
## site33R -0.3966719 0.6725547 0.3523673 -1.126 0.260
## site34R -0.1931466 0.8243611 0.3301659 -0.585 0.559
## site35R -0.1699420 0.8437138 0.3485186 -0.488 0.626
## site36R -0.2670486 0.7656359 0.3636977 -0.734 0.463
## site37R -0.2471685 0.7810091 0.3584277 -0.690 0.490
## site38R -0.3422450 0.7101742 0.3474319 -0.985 0.325
## site39R -0.2614249 0.7699537 0.3735101 -0.700 0.484
## site40R 0.0501533 1.0514323 0.3963693 0.127 0.899
## site41R -0.2687248 0.7643536 0.3846476 -0.699 0.485
## site42R -0.0227469 0.9775099 0.3821024 -0.060 0.953
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## PM 1.0144 0.9858 0.9638 1.068
## dx_yr 1.1127 0.8987 1.0790 1.148
## age_dx 0.9997 1.0003 0.9920 1.007
## sexM 1.0630 0.9407 0.9244 1.222
## dich_RaceNon-White 0.9393 1.0646 0.7333 1.203
## smokeHxEver 1.0440 0.9579 0.9197 1.185
## disadv 0.9496 1.0531 0.7623 1.183
## site02R 1.7650 0.5666 0.8757 3.557
## site03R 0.7626 1.3114 0.3772 1.542
## site04R 0.7332 1.3639 0.2917 1.843
## site05R 0.6685 1.4958 0.3060 1.461
## site06R 0.9103 1.0986 0.4598 1.802
## site07R 0.6833 1.4634 0.3598 1.298
## site08R NA NA NA NA
## site09R 0.8913 1.1220 0.4424 1.796
## site10R 0.8044 1.2431 0.3672 1.762
## site11R 0.8544 1.1704 0.4481 1.629
## site12R 0.8745 1.1435 0.4415 1.732
## site13R 0.6868 1.4560 0.3650 1.293
## site14R 0.6383 1.5667 0.1774 2.296
## site15R 0.9637 1.0377 0.4831 1.922
## site16R 1.0715 0.9332 0.5283 2.174
## site17R 0.9394 1.0645 0.4411 2.001
## site18R 0.7144 1.3998 0.3623 1.409
## site19R 0.9553 1.0468 0.4694 1.944
## site20R 0.7259 1.3775 0.3550 1.485
## site21R 0.6898 1.4496 0.3493 1.362
## site22R 0.8627 1.1592 0.4519 1.647
## site23R 0.7803 1.2815 0.3969 1.534
## site24R 0.8145 1.2278 0.4131 1.606
## site25R 0.9336 1.0711 0.4753 1.834
## site26R 0.6336 1.5784 0.3112 1.290
## site27R 0.8876 1.1266 0.1946 4.048
## site28R 0.8184 1.2219 0.3321 2.017
## site29R 0.7038 1.4209 0.2925 1.693
## site30R 0.7309 1.3682 0.3581 1.492
## site31R 0.6902 1.4488 0.2806 1.698
## site32R 0.6505 1.5373 0.2998 1.412
## site33R 0.6726 1.4869 0.3371 1.342
## site34R 0.8244 1.2131 0.4316 1.575
## site35R 0.8437 1.1852 0.4261 1.671
## site36R 0.7656 1.3061 0.3754 1.562
## site37R 0.7810 1.2804 0.3869 1.577
## site38R 0.7102 1.4081 0.3594 1.403
## site39R 0.7700 1.2988 0.3703 1.601
## site40R 1.0514 0.9511 0.4835 2.287
## site41R 0.7644 1.3083 0.3597 1.624
## site42R 0.9775 1.0230 0.4622 2.067
##
## Concordance= 0.591 (se = 0.009 )
## Likelihood ratio test= 111.5 on 47 df, p=4e-07
## Wald test = 110.2 on 47 df, p=5e-07
## Score (logrank) test = 114.5 on 47 df, p=1e-07
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ PM + dx_yr + site, data=CARE_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ PM + dx_yr + site,
## data = CARE_IPF, id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## PM -0.02785 0.97253 0.02487 -1.120 0.262773
## dx_yr 0.81343 2.25563 0.03231 25.175 < 2e-16 ***
## site102 0.17913 1.19618 0.15161 1.182 0.237393
## site103 0.47621 1.60996 0.12588 3.783 0.000155 ***
## site104 0.33123 1.39268 0.14417 2.297 0.021591 *
## site105 0.05790 1.05961 0.12611 0.459 0.646148
## site106 0.15043 1.16234 0.12170 1.236 0.216443
## site107 0.10144 1.10676 0.19748 0.514 0.607493
## site108 -0.43257 0.64884 0.26539 -1.630 0.103118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## PM 0.9725 1.0282 0.9263 1.021
## dx_yr 2.2556 0.4433 2.1172 2.403
## site102 1.1962 0.8360 0.8887 1.610
## site103 1.6100 0.6211 1.2580 2.060
## site104 1.3927 0.7180 1.0499 1.847
## site105 1.0596 0.9437 0.8276 1.357
## site106 1.1623 0.8603 0.9157 1.475
## site107 1.1068 0.9035 0.7515 1.630
## site108 0.6488 1.5412 0.3857 1.092
##
## Concordance= 0.774 (se = 0.01 )
## Likelihood ratio test= 1053 on 9 df, p=<2e-16
## Wald test = 667.6 on 9 df, p=<2e-16
## Score (logrank) test = 586.3 on 9 df, p=<2e-16
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ PM + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=CARE_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ PM + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = CARE_IPF,
## id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## PM -0.033485 0.967069 0.025449 -1.316 0.188246
## dx_yr 0.812722 2.254036 0.032467 25.032 < 2e-16 ***
## age_dx 0.012291 1.012367 0.004506 2.727 0.006382 **
## sexF -0.043222 0.957699 0.077049 -0.561 0.574823
## dich_RaceNon-White -0.145971 0.864183 0.110402 -1.322 0.186111
## smokeHxFormer 0.001762 1.001764 0.081519 0.022 0.982751
## smokeHxAlways 0.059700 1.061518 0.168799 0.354 0.723583
## smokeHxUnknown 0.421664 1.524495 0.734804 0.574 0.566073
## disadv -0.009229 0.990814 0.129610 -0.071 0.943235
## site102 0.151231 1.163265 0.154045 0.982 0.326233
## site103 0.483593 1.621891 0.129013 3.748 0.000178 ***
## site104 0.331633 1.393242 0.145263 2.283 0.022431 *
## site105 0.074673 1.077531 0.129274 0.578 0.563512
## site106 0.122846 1.130710 0.122826 1.000 0.317231
## site107 0.037946 1.038675 0.204876 0.185 0.853060
## site108 -0.435598 0.646878 0.268748 -1.621 0.105052
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## PM 0.9671 1.0341 0.9200 1.017
## dx_yr 2.2540 0.4436 2.1151 2.402
## age_dx 1.0124 0.9878 1.0035 1.021
## sexF 0.9577 1.0442 0.8235 1.114
## dich_RaceNon-White 0.8642 1.1572 0.6960 1.073
## smokeHxFormer 1.0018 0.9982 0.8538 1.175
## smokeHxAlways 1.0615 0.9420 0.7625 1.478
## smokeHxUnknown 1.5245 0.6560 0.3611 6.436
## disadv 0.9908 1.0093 0.7685 1.277
## site102 1.1633 0.8596 0.8601 1.573
## site103 1.6219 0.6166 1.2595 2.089
## site104 1.3932 0.7178 1.0480 1.852
## site105 1.0775 0.9280 0.8364 1.388
## site106 1.1307 0.8844 0.8888 1.438
## site107 1.0387 0.9628 0.6952 1.552
## site108 0.6469 1.5459 0.3820 1.095
##
## Concordance= 0.776 (se = 0.01 )
## Likelihood ratio test= 1065 on 16 df, p=<2e-16
## Wald test = 674.9 on 16 df, p=<2e-16
## Score (logrank) test = 593.2 on 16 df, p=<2e-16
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ PM + dx_yr + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ PM + dx_yr + site,
## data = All_IPF, id = ID, cluster = cohort)
##
## n= 123367, number of events= 2749
## (723 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## PM 0.08938 1.09349 0.01272 0.02513 3.557 0.000376 ***
## dx_yr 0.13165 1.14071 0.00799 0.07410 1.777 0.075627 .
## site02R 0.39000 1.47698 0.33677 0.11493 3.393 0.000690 ***
## site03R -0.26172 0.76972 0.34707 0.02219 -11.796 < 2e-16 ***
## site04R -0.54224 0.58144 0.45158 0.13461 -4.028 5.62e-05 ***
## site05R -0.71039 0.49145 0.37245 0.12053 -5.894 3.77e-09 ***
## site06R -0.27350 0.76071 0.32654 0.09366 -2.920 0.003497 **
## site07R -0.55793 0.57239 0.30653 0.13255 -4.209 2.56e-05 ***
## site09R -0.18211 0.83351 0.34035 0.05269 -3.456 0.000548 ***
## site1 -0.04754 0.95358 0.28447 0.12715 -0.374 0.708512
## site101 -0.24892 0.77964 0.29391 0.14116 -1.763 0.077819 .
## site102 -0.18954 0.82734 0.29799 0.09288 -2.041 0.041281 *
## site103 0.13423 1.14366 0.28632 0.07548 1.778 0.075324 .
## site104 -0.16500 0.84790 0.29469 0.07366 -2.240 0.025094 *
## site105 -0.06306 0.93889 0.29029 0.14070 -0.448 0.654014
## site106 -0.42584 0.65322 0.28789 0.12037 -3.538 0.000403 ***
## site107 0.07045 1.07299 0.32733 0.14843 0.475 0.635063
## site108 0.10534 1.11109 0.36947 0.18185 0.579 0.562404
## site10R -0.24856 0.77992 0.38542 0.07079 -3.511 0.000446 ***
## site11R -0.33972 0.71197 0.30189 0.10312 -3.294 0.000987 ***
## site12R -0.16650 0.84662 0.33400 0.03958 -4.206 2.60e-05 ***
## site13R -0.51702 0.59630 0.30330 0.11929 -4.334 1.46e-05 ***
## site14R -0.60931 0.54373 0.64169 0.12406 -4.911 9.05e-07 ***
## site15R -0.17097 0.84285 0.33165 0.06002 -2.848 0.004394 **
## site16R -0.05003 0.95120 0.34541 0.05630 -0.889 0.374175
## site17R -0.27263 0.76137 0.36269 0.08542 -3.192 0.001414 **
## site18R -0.50842 0.60145 0.32342 0.17325 -2.935 0.003339 **
## site19R 0.02922 1.02965 0.35000 0.01152 2.537 0.011193 *
## site20R -0.43820 0.64520 0.34786 0.06160 -7.114 1.13e-12 ***
## site21R -0.62871 0.53328 0.32243 0.11748 -5.352 8.72e-08 ***
## site22R -0.33548 0.71499 0.30735 0.08213 -4.085 4.42e-05 ***
## site23R -0.37855 0.68485 0.32384 0.06814 -5.555 2.77e-08 ***
## site24R -0.26057 0.77061 0.33092 0.06113 -4.263 2.02e-05 ***
## site25R -0.25412 0.77560 0.32685 0.10257 -2.477 0.013232 *
## site26R -0.60635 0.54534 0.34552 0.10949 -5.538 3.06e-08 ***
## site27R -0.25294 0.77652 0.76036 0.13914 -1.818 0.069085 .
## site28R -0.51833 0.59551 0.43696 0.11709 -4.427 9.57e-06 ***
## site29R -0.45868 0.63212 0.43420 0.07187 -6.382 1.74e-10 ***
## site30R -0.60641 0.54531 0.33514 0.16363 -3.706 0.000211 ***
## site31R -0.40898 0.66433 0.44953 0.06549 -6.245 4.23e-10 ***
## site32R -0.52348 0.59246 0.36930 0.07090 -7.383 1.54e-13 ***
## site33R -0.53464 0.58588 0.33312 0.10677 -5.007 5.52e-07 ***
## site34R -0.24717 0.78101 0.31366 0.05295 -4.668 3.04e-06 ***
## site35R -0.15706 0.85465 0.33385 0.02707 -5.802 6.56e-09 ***
## site36R -0.50791 0.60176 0.34004 0.09347 -5.434 5.52e-08 ***
## site37R -0.47732 0.62045 0.33637 0.10662 -4.477 7.57e-06 ***
## site38R -0.47973 0.61895 0.32993 0.06720 -7.139 9.41e-13 ***
## site39R -0.30542 0.73682 0.36027 0.04093 -7.463 8.48e-14 ***
## site40R -0.45271 0.63590 0.35413 0.23145 -1.956 0.050464 .
## site41R -0.42550 0.65344 0.36966 0.07719 -5.513 3.53e-08 ***
## site42R -0.19531 0.82257 0.36203 0.08142 -2.399 0.016446 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## PM 1.0935 0.9145 1.0409 1.1487
## dx_yr 1.1407 0.8767 0.9865 1.3190
## site02R 1.4770 0.6771 1.1791 1.8501
## site03R 0.7697 1.2992 0.7370 0.8039
## site04R 0.5814 1.7199 0.4466 0.7570
## site05R 0.4915 2.0348 0.3881 0.6224
## site06R 0.7607 1.3146 0.6331 0.9140
## site07R 0.5724 1.7470 0.4414 0.7422
## site09R 0.8335 1.1997 0.7517 0.9242
## site1 0.9536 1.0487 0.7432 1.2234
## site101 0.7796 1.2826 0.5912 1.0281
## site102 0.8273 1.2087 0.6896 0.9925
## site103 1.1437 0.8744 0.9864 1.3260
## site104 0.8479 1.1794 0.7339 0.9796
## site105 0.9389 1.0651 0.7126 1.2370
## site106 0.6532 1.5309 0.5159 0.8270
## site107 1.0730 0.9320 0.8021 1.4353
## site108 1.1111 0.9000 0.7780 1.5869
## site10R 0.7799 1.2822 0.6789 0.8960
## site11R 0.7120 1.4046 0.5817 0.8714
## site12R 0.8466 1.1812 0.7834 0.9149
## site13R 0.5963 1.6770 0.4720 0.7534
## site14R 0.5437 1.8392 0.4264 0.6934
## site15R 0.8428 1.1865 0.7493 0.9481
## site16R 0.9512 1.0513 0.8518 1.0622
## site17R 0.7614 1.3134 0.6440 0.9001
## site18R 0.6014 1.6627 0.4283 0.8446
## site19R 1.0296 0.9712 1.0067 1.0532
## site20R 0.6452 1.5499 0.5718 0.7280
## site21R 0.5333 1.8752 0.4236 0.6714
## site22R 0.7150 1.3986 0.6087 0.8399
## site23R 0.6849 1.4602 0.5992 0.7827
## site24R 0.7706 1.2977 0.6836 0.8687
## site25R 0.7756 1.2893 0.6344 0.9483
## site26R 0.5453 1.8337 0.4400 0.6759
## site27R 0.7765 1.2878 0.5912 1.0200
## site28R 0.5955 1.6792 0.4734 0.7491
## site29R 0.6321 1.5820 0.5491 0.7277
## site30R 0.5453 1.8338 0.3957 0.7515
## site31R 0.6643 1.5053 0.5843 0.7553
## site32R 0.5925 1.6879 0.5156 0.6808
## site33R 0.5859 1.7068 0.4753 0.7223
## site34R 0.7810 1.2804 0.7040 0.8664
## site35R 0.8546 1.1701 0.8105 0.9012
## site36R 0.6018 1.6618 0.5010 0.7227
## site37R 0.6204 1.6117 0.5034 0.7646
## site38R 0.6189 1.6156 0.5426 0.7061
## site39R 0.7368 1.3572 0.6800 0.7984
## site40R 0.6359 1.5726 0.4040 1.0009
## site41R 0.6534 1.5304 0.5617 0.7602
## site42R 0.8226 1.2157 0.7012 0.9649
##
## Concordance= 0.598 (se = 0.042 )
## Likelihood ratio test= 393.1 on 51 df, p=<2e-16
## Wald test = 12.65 on 51 df, p=1
## Score (logrank) test = 383.3 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ PM + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ PM + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = All_IPF,
## id = ID, cluster = cohort)
##
## n= 121241, number of events= 2709
## (2849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## PM 0.091837 1.096187 0.012965 0.025583 3.590 0.000331 ***
## dx_yr 0.129425 1.138174 0.008351 0.078994 1.638 0.101333
## age_dx 0.003620 1.003627 0.002441 0.001862 1.945 0.051795 .
## sexF -0.147133 0.863179 0.044856 0.079342 -1.854 0.063680 .
## dich_RaceNon-White 0.019630 1.019824 0.068805 0.065474 0.300 0.764319
## smokeHxFormer 0.102733 1.108196 0.059312 0.024246 4.237 2.26e-05 ***
## smokeHxAlways -0.049278 0.951916 0.138517 0.076317 -0.646 0.518467
## smokeHxUnknown 0.313831 1.368658 0.153426 0.212565 1.476 0.139836
## smokeHxEver 0.035221 1.035848 0.064338 0.005610 6.278 3.42e-10 ***
## disadv 0.038805 1.039568 0.070322 0.138014 0.281 0.778580
## site02R 0.357983 1.430441 0.347672 0.120657 2.967 0.003008 **
## site03R -0.249332 0.779322 0.357337 0.030023 -8.305 < 2e-16 ***
## site04R -0.553654 0.574845 0.460054 0.140262 -3.947 7.90e-05 ***
## site05R -0.727255 0.483234 0.383148 0.103508 -7.026 2.12e-12 ***
## site06R -0.297273 0.742841 0.339386 0.106290 -2.797 0.005161 **
## site07R -0.578704 0.560625 0.318322 0.126572 -4.572 4.83e-06 ***
## site09R -0.240143 0.786515 0.352805 0.037444 -6.413 1.42e-10 ***
## site1 -0.134474 0.874176 0.300964 0.144984 -0.928 0.353663
## site101 -0.316675 0.728568 0.309073 0.132713 -2.386 0.017025 *
## site102 -0.267425 0.765348 0.313812 0.102359 -2.613 0.008985 **
## site103 0.050872 1.052189 0.301936 0.049705 1.023 0.306073
## site104 -0.238952 0.787453 0.310440 0.052748 -4.530 5.90e-06 ***
## site105 -0.135963 0.872875 0.307531 0.126656 -1.073 0.283054
## site106 -0.520068 0.594480 0.303967 0.107566 -4.835 1.33e-06 ***
## site107 -0.034024 0.966549 0.343073 0.126621 -0.269 0.788156
## site108 0.019986 1.020187 0.381834 0.170829 0.117 0.906863
## site10R -0.319597 0.726441 0.396305 0.052045 -6.141 8.21e-10 ***
## site11R -0.389621 0.677314 0.316340 0.088902 -4.383 1.17e-05 ***
## site12R -0.196957 0.821226 0.345268 0.035094 -5.612 2.00e-08 ***
## site13R -0.567420 0.566986 0.315020 0.120650 -4.703 2.56e-06 ***
## site14R -0.633290 0.530842 0.647593 0.082096 -7.714 1.22e-14 ***
## site15R -0.222817 0.800261 0.343927 0.052617 -4.235 2.29e-05 ***
## site16R -0.064642 0.937403 0.355638 0.042196 -1.532 0.125534
## site17R -0.301495 0.739712 0.373681 0.066959 -4.503 6.71e-06 ***
## site18R -0.552419 0.575556 0.335527 0.149705 -3.690 0.000224 ***
## site19R -0.020529 0.979680 0.360824 0.023170 -0.886 0.375604
## site20R -0.450495 0.637312 0.359322 0.041368 -10.890 < 2e-16 ***
## site21R -0.681061 0.506080 0.333935 0.110347 -6.172 6.74e-10 ***
## site22R -0.362686 0.695805 0.319455 0.070735 -5.127 2.94e-07 ***
## site23R -0.450334 0.637415 0.337731 0.061302 -7.346 2.04e-13 ***
## site24R -0.282946 0.753561 0.342679 0.048680 -5.812 6.16e-09 ***
## site25R -0.286930 0.750564 0.338773 0.114357 -2.509 0.012105 *
## site26R -0.657131 0.518336 0.356471 0.091405 -7.189 6.52e-13 ***
## site27R -0.258245 0.772406 0.767858 0.138845 -1.860 0.062893 .
## site28R -0.536647 0.584705 0.445890 0.101618 -5.281 1.28e-07 ***
## site29R -0.475255 0.621726 0.443492 0.090122 -5.273 1.34e-07 ***
## site30R -0.689884 0.501634 0.347458 0.164160 -4.203 2.64e-05 ***
## site31R -0.453042 0.635691 0.457746 0.058538 -7.739 1.00e-14 ***
## site32R -0.603758 0.546753 0.389816 0.067813 -8.903 < 2e-16 ***
## site33R -0.576704 0.561747 0.346291 0.083396 -6.915 4.67e-12 ***
## site34R -0.270578 0.762938 0.326435 0.049271 -5.492 3.98e-08 ***
## site35R -0.219282 0.803095 0.346941 0.018932 -11.583 < 2e-16 ***
## site36R -0.529378 0.588971 0.351387 0.080397 -6.585 4.56e-11 ***
## site37R -0.509113 0.601029 0.347352 0.103301 -4.928 8.29e-07 ***
## site38R -0.498822 0.607245 0.341280 0.047607 -10.478 < 2e-16 ***
## site39R -0.359653 0.697919 0.370984 0.035479 -10.137 < 2e-16 ***
## site40R -0.523215 0.592612 0.364591 0.228534 -2.289 0.022054 *
## site41R -0.477021 0.620629 0.378999 0.073324 -6.506 7.74e-11 ***
## site42R -0.235446 0.790218 0.372341 0.072252 -3.259 0.001119 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## PM 1.0962 0.9123 1.0426 1.1526
## dx_yr 1.1382 0.8786 0.9749 1.3288
## age_dx 1.0036 0.9964 1.0000 1.0073
## sexF 0.8632 1.1585 0.7389 1.0084
## dich_RaceNon-White 1.0198 0.9806 0.8970 1.1595
## smokeHxFormer 1.1082 0.9024 1.0568 1.1621
## smokeHxAlways 0.9519 1.0505 0.8197 1.1055
## smokeHxUnknown 1.3687 0.7306 0.9023 2.0760
## smokeHxEver 1.0358 0.9654 1.0245 1.0473
## disadv 1.0396 0.9619 0.7932 1.3625
## site02R 1.4304 0.6991 1.1292 1.8121
## site03R 0.7793 1.2832 0.7348 0.8266
## site04R 0.5748 1.7396 0.4367 0.7567
## site05R 0.4832 2.0694 0.3945 0.5919
## site06R 0.7428 1.3462 0.6031 0.9149
## site07R 0.5606 1.7837 0.4375 0.7185
## site09R 0.7865 1.2714 0.7309 0.8464
## site1 0.8742 1.1439 0.6579 1.1615
## site101 0.7286 1.3726 0.5617 0.9450
## site102 0.7653 1.3066 0.6262 0.9354
## site103 1.0522 0.9504 0.9545 1.1599
## site104 0.7875 1.2699 0.7101 0.8732
## site105 0.8729 1.1456 0.6810 1.1188
## site106 0.5945 1.6821 0.4815 0.7340
## site107 0.9665 1.0346 0.7541 1.2388
## site108 1.0202 0.9802 0.7299 1.4259
## site10R 0.7264 1.3766 0.6560 0.8045
## site11R 0.6773 1.4764 0.5690 0.8062
## site12R 0.8212 1.2177 0.7666 0.8797
## site13R 0.5670 1.7637 0.4476 0.7182
## site14R 0.5308 1.8838 0.4519 0.6235
## site15R 0.8003 1.2496 0.7218 0.8872
## site16R 0.9374 1.0668 0.8630 1.0182
## site17R 0.7397 1.3519 0.6487 0.8434
## site18R 0.5756 1.7375 0.4292 0.7718
## site19R 0.9797 1.0207 0.9362 1.0252
## site20R 0.6373 1.5691 0.5877 0.6911
## site21R 0.5061 1.9760 0.4077 0.6283
## site22R 0.6958 1.4372 0.6057 0.7993
## site23R 0.6374 1.5688 0.5653 0.7188
## site24R 0.7536 1.3270 0.6850 0.8290
## site25R 0.7506 1.3323 0.5999 0.9391
## site26R 0.5183 1.9292 0.4333 0.6200
## site27R 0.7724 1.2947 0.5884 1.0140
## site28R 0.5847 1.7103 0.4791 0.7136
## site29R 0.6217 1.6084 0.5211 0.7418
## site30R 0.5016 1.9935 0.3636 0.6920
## site31R 0.6357 1.5731 0.5668 0.7130
## site32R 0.5468 1.8290 0.4787 0.6245
## site33R 0.5617 1.7802 0.4770 0.6615
## site34R 0.7629 1.3107 0.6927 0.8403
## site35R 0.8031 1.2452 0.7738 0.8335
## site36R 0.5890 1.6979 0.5031 0.6895
## site37R 0.6010 1.6638 0.4909 0.7359
## site38R 0.6072 1.6468 0.5531 0.6666
## site39R 0.6979 1.4328 0.6510 0.7482
## site40R 0.5926 1.6874 0.3787 0.9275
## site41R 0.6206 1.6113 0.5375 0.7165
## site42R 0.7902 1.2655 0.6859 0.9104
##
## Concordance= 0.603 (se = 0.038 )
## Likelihood ratio test= 426 on 59 df, p=<2e-16
## Wald test = 12.89 on 59 df, p=1
## Score (logrank) test = 421.1 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ SO4 + dx_yr, data=Simm_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SO4 + dx_yr, data = Simm_IPF,
## id = ID)
##
## n= 31982, number of events= 695
## (446 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SO4 0.09060 1.09483 0.06155 1.472 0.14099
## dx_yr 0.04545 1.04650 0.01732 2.624 0.00868 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SO4 1.095 0.9134 0.9704 1.235
## dx_yr 1.046 0.9556 1.0116 1.083
##
## Concordance= 0.52 (se = 0.013 )
## Likelihood ratio test= 9.8 on 2 df, p=0.007
## Wald test = 9.57 on 2 df, p=0.008
## Score (logrank) test = 9.59 on 2 df, p=0.008
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ SO4 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv, data=Simm_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SO4 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv, data = Simm_IPF,
## id = ID)
##
## n= 30488, number of events= 670
## (1940 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SO4 0.083150 1.086704 0.064814 1.283 0.19953
## dx_yr 0.031789 1.032299 0.018531 1.715 0.08626 .
## age_dx 0.001624 1.001625 0.004573 0.355 0.72252
## sexF -0.379354 0.684303 0.088721 -4.276 1.9e-05 ***
## dich_RaceNon-White 0.173916 1.189956 0.129043 1.348 0.17774
## smokeHxFormer 0.081480 1.084892 0.091977 0.886 0.37569
## smokeHxAlways -0.402797 0.668447 0.266655 -1.511 0.13090
## smokeHxUnknown 0.484776 1.623811 0.170538 2.843 0.00447 **
## disadv 0.373185 1.452352 0.134038 2.784 0.00537 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SO4 1.0867 0.9202 0.9571 1.2339
## dx_yr 1.0323 0.9687 0.9955 1.0705
## age_dx 1.0016 0.9984 0.9927 1.0106
## sexF 0.6843 1.4613 0.5751 0.8143
## dich_RaceNon-White 1.1900 0.8404 0.9240 1.5324
## smokeHxFormer 1.0849 0.9218 0.9059 1.2992
## smokeHxAlways 0.6684 1.4960 0.3964 1.1273
## smokeHxUnknown 1.6238 0.6158 1.1624 2.2683
## disadv 1.4524 0.6885 1.1168 1.8887
##
## Concordance= 0.59 (se = 0.012 )
## Likelihood ratio test= 53.29 on 9 df, p=3e-08
## Wald test = 53.64 on 9 df, p=2e-08
## Score (logrank) test = 54.35 on 9 df, p=2e-08
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ SO4 + dx_yr + site, data=PFF_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SO4 + dx_yr +
## site, data = PFF_IPF, id = ID)
##
## n= 54499, number of events= 1146
## (217 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SO4 0.29874 1.34816 0.11789 2.534 0.0113 *
## dx_yr 0.15006 1.16190 0.02293 6.543 6.01e-11 ***
## site02R 0.51183 1.66834 0.34011 1.505 0.1324
## site03R -0.29789 0.74239 0.34775 -0.857 0.3917
## site04R -0.57325 0.56369 0.47156 -1.216 0.2241
## site05R -0.86069 0.42287 0.42965 -2.003 0.0452 *
## site06R -0.45893 0.63196 0.36799 -1.247 0.2124
## site07R -0.67961 0.50681 0.33857 -2.007 0.0447 *
## site08R NA NA 0.00000 NA NA
## site09R -0.29366 0.74553 0.35711 -0.822 0.4109
## site10R -0.35920 0.69823 0.39396 -0.912 0.3619
## site11R -0.47857 0.61967 0.34193 -1.400 0.1616
## site12R -0.25982 0.77119 0.34126 -0.761 0.4464
## site13R -0.69525 0.49895 0.34163 -2.035 0.0418 *
## site14R -0.75645 0.46933 0.66132 -1.144 0.2527
## site15R -0.32279 0.72413 0.35988 -0.897 0.3698
## site16R -0.17098 0.84284 0.36529 -0.468 0.6397
## site17R -0.39131 0.67617 0.39804 -0.983 0.3256
## site18R -0.66029 0.51670 0.35747 -1.847 0.0647 .
## site19R -0.05029 0.95096 0.35026 -0.144 0.8858
## site20R -0.55355 0.57491 0.36740 -1.507 0.1319
## site21R -0.71054 0.49138 0.36104 -1.968 0.0491 *
## site22R -0.44350 0.64178 0.34101 -1.301 0.1934
## site23R -0.52209 0.59328 0.35326 -1.478 0.1394
## site24R -0.32669 0.72131 0.33912 -0.963 0.3354
## site25R -0.10926 0.89650 0.32762 -0.333 0.7388
## site26R -0.79588 0.45119 0.37927 -2.098 0.0359 *
## site27R -0.41947 0.65739 0.77908 -0.538 0.5903
## site28R -0.64660 0.52382 0.48181 -1.342 0.1796
## site29R -0.57963 0.56010 0.44954 -1.289 0.1973
## site30R -0.59736 0.55026 0.36543 -1.635 0.1021
## site31R -0.65652 0.51866 0.46931 -1.399 0.1618
## site32R -0.42321 0.65494 0.36991 -1.144 0.2526
## site33R -0.74074 0.47676 0.36927 -2.006 0.0449 *
## site34R -0.45032 0.63742 0.33614 -1.340 0.1803
## site35R -0.30782 0.73505 0.34287 -0.898 0.3693
## site36R -0.69292 0.50011 0.39089 -1.773 0.0763 .
## site37R -0.62266 0.53652 0.38036 -1.637 0.1016
## site38R -0.64687 0.52368 0.35936 -1.800 0.0719 .
## site39R -0.24234 0.78479 0.36045 -0.672 0.5014
## site40R -0.10430 0.90095 0.35966 -0.290 0.7718
## site41R -0.51508 0.59745 0.38866 -1.325 0.1851
## site42R -0.34951 0.70503 0.39792 -0.878 0.3798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SO4 1.3482 0.7418 1.0700 1.6986
## dx_yr 1.1619 0.8607 1.1108 1.2153
## site02R 1.6683 0.5994 0.8566 3.2493
## site03R 0.7424 1.3470 0.3755 1.4677
## site04R 0.5637 1.7740 0.2237 1.4205
## site05R 0.4229 2.3648 0.1822 0.9816
## site06R 0.6320 1.5824 0.3072 1.2999
## site07R 0.5068 1.9731 0.2610 0.9841
## site08R NA NA NA NA
## site09R 0.7455 1.3413 0.3702 1.5012
## site10R 0.6982 1.4322 0.3226 1.5113
## site11R 0.6197 1.6138 0.3170 1.2112
## site12R 0.7712 1.2967 0.3951 1.5054
## site13R 0.4990 2.0042 0.2554 0.9747
## site14R 0.4693 2.1307 0.1284 1.7155
## site15R 0.7241 1.3810 0.3577 1.4660
## site16R 0.8428 1.1865 0.4119 1.7246
## site17R 0.6762 1.4789 0.3099 1.4753
## site18R 0.5167 1.9354 0.2564 1.0412
## site19R 0.9510 1.0516 0.4786 1.8893
## site20R 0.5749 1.7394 0.2798 1.1812
## site21R 0.4914 2.0351 0.2422 0.9971
## site22R 0.6418 1.5582 0.3289 1.2522
## site23R 0.5933 1.6855 0.2969 1.1856
## site24R 0.7213 1.3864 0.3711 1.4021
## site25R 0.8965 1.1155 0.4717 1.7038
## site26R 0.4512 2.2164 0.2145 0.9488
## site27R 0.6574 1.5212 0.1428 3.0268
## site28R 0.5238 1.9090 0.2037 1.3468
## site29R 0.5601 1.7854 0.2321 1.3518
## site30R 0.5503 1.8173 0.2689 1.1262
## site31R 0.5187 1.9281 0.2067 1.3012
## site32R 0.6549 1.5268 0.3172 1.3523
## site33R 0.4768 2.0975 0.2312 0.9832
## site34R 0.6374 1.5688 0.3298 1.2318
## site35R 0.7350 1.3605 0.3754 1.4394
## site36R 0.5001 1.9995 0.2325 1.0760
## site37R 0.5365 1.8639 0.2546 1.1307
## site38R 0.5237 1.9096 0.2589 1.0591
## site39R 0.7848 1.2742 0.3872 1.5906
## site40R 0.9010 1.1099 0.4452 1.8233
## site41R 0.5975 1.6738 0.2789 1.2798
## site42R 0.7050 1.4184 0.3232 1.5379
##
## Concordance= 0.59 (se = 0.009 )
## Likelihood ratio test= 116.8 on 42 df, p=6e-09
## Wald test = 115.6 on 42 df, p=8e-09
## Score (logrank) test = 119.4 on 42 df, p=2e-09
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ SO4 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=PFF_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SO4 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = PFF_IPF,
## id = ID)
##
## n= 53867, number of events= 1131
## (849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SO4 0.3064731 1.3586250 0.1195415 2.564 0.0104 *
## dx_yr 0.1500659 1.1619108 0.0234586 6.397 1.58e-10 ***
## age_dx -0.0001734 0.9998266 0.0039376 -0.044 0.9649
## sexM 0.0627253 1.0647343 0.0713136 0.880 0.3791
## dich_RaceNon-White -0.0749020 0.9278344 0.1263413 -0.593 0.5533
## smokeHxEver 0.0422050 1.0431083 0.0646237 0.653 0.5137
## disadv -0.0354140 0.9652058 0.1121920 -0.316 0.7523
## site02R 0.4498402 1.5680615 0.3530122 1.274 0.2026
## site03R -0.3388478 0.7125909 0.3604354 -0.940 0.3472
## site04R -0.6439857 0.5251950 0.4832468 -1.333 0.1827
## site05R -0.9274047 0.3955790 0.4440119 -2.089 0.0367 *
## site06R -0.5194282 0.5948606 0.3858724 -1.346 0.1783
## site07R -0.7407730 0.4767452 0.3541161 -2.092 0.0364 *
## site08R NA NA 0.0000000 NA NA
## site09R -0.3890314 0.6777130 0.3726994 -1.044 0.2966
## site10R -0.4351733 0.6471525 0.4085917 -1.065 0.2869
## site11R -0.5610823 0.5705912 0.3620088 -1.550 0.1212
## site12R -0.3211570 0.7253093 0.3562261 -0.902 0.3673
## site13R -0.7707461 0.4626677 0.3564345 -2.162 0.0306 *
## site14R -0.8345537 0.4340682 0.6698827 -1.246 0.2128
## site15R -0.3940048 0.6743508 0.3774856 -1.044 0.2966
## site16R -0.2290276 0.7953066 0.3775767 -0.607 0.5441
## site17R -0.4691302 0.6255461 0.4133164 -1.135 0.2564
## site18R -0.7184589 0.4875030 0.3750471 -1.916 0.0554 .
## site19R -0.1098669 0.8959534 0.3630151 -0.303 0.7622
## site20R -0.6226339 0.5365294 0.3826298 -1.627 0.1037
## site21R -0.7757126 0.4603756 0.3766455 -2.060 0.0394 *
## site22R -0.5140142 0.5980899 0.3564887 -1.442 0.1493
## site23R -0.6063407 0.5453428 0.3706927 -1.636 0.1019
## site24R -0.3958461 0.6731103 0.3537055 -1.119 0.2631
## site25R -0.1550409 0.8563802 0.3409623 -0.455 0.6493
## site26R -0.8523226 0.4264234 0.3944782 -2.161 0.0307 *
## site27R -0.5388276 0.5834318 0.7909146 -0.681 0.4957
## site28R -0.7030685 0.4950639 0.4958213 -1.418 0.1562
## site29R -0.6488605 0.5226410 0.4620503 -1.404 0.1602
## site30R -0.6698455 0.5117876 0.3797116 -1.764 0.0777 .
## site31R -0.7250954 0.4842783 0.4798081 -1.511 0.1307
## site32R -0.5100427 0.6004699 0.3926284 -1.299 0.1939
## site33R -0.7981224 0.4501734 0.3857167 -2.069 0.0385 *
## site34R -0.5115033 0.5995935 0.3528854 -1.449 0.1472
## site35R -0.3833053 0.6816048 0.3581951 -1.070 0.2846
## site36R -0.7504257 0.4721655 0.4077344 -1.840 0.0657 .
## site37R -0.6864738 0.5033478 0.3949042 -1.738 0.0822 .
## site38R -0.7037955 0.4947041 0.3745071 -1.879 0.0602 .
## site39R -0.3063334 0.7361411 0.3731401 -0.821 0.4117
## site40R -0.1477716 0.8626281 0.3711330 -0.398 0.6905
## site41R -0.5704288 0.5652830 0.4003813 -1.425 0.1542
## site42R -0.4327032 0.6487530 0.4114806 -1.052 0.2930
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SO4 1.3586 0.7360 1.0748 1.7173
## dx_yr 1.1619 0.8607 1.1097 1.2166
## age_dx 0.9998 1.0002 0.9921 1.0076
## sexM 1.0647 0.9392 0.9258 1.2245
## dich_RaceNon-White 0.9278 1.0778 0.7243 1.1885
## smokeHxEver 1.0431 0.9587 0.9190 1.1840
## disadv 0.9652 1.0360 0.7747 1.2026
## site02R 1.5681 0.6377 0.7850 3.1322
## site03R 0.7126 1.4033 0.3516 1.4443
## site04R 0.5252 1.9041 0.2037 1.3541
## site05R 0.3956 2.5279 0.1657 0.9444
## site06R 0.5949 1.6811 0.2792 1.2673
## site07R 0.4767 2.0976 0.2382 0.9544
## site08R NA NA NA NA
## site09R 0.6777 1.4756 0.3264 1.4070
## site10R 0.6472 1.5452 0.2905 1.4415
## site11R 0.5706 1.7526 0.2807 1.1600
## site12R 0.7253 1.3787 0.3608 1.4580
## site13R 0.4627 2.1614 0.2301 0.9304
## site14R 0.4341 2.3038 0.1168 1.6135
## site15R 0.6744 1.4829 0.3218 1.4132
## site16R 0.7953 1.2574 0.3794 1.6670
## site17R 0.6255 1.5986 0.2783 1.4063
## site18R 0.4875 2.0513 0.2337 1.0168
## site19R 0.8960 1.1161 0.4398 1.8251
## site20R 0.5365 1.8638 0.2535 1.1358
## site21R 0.4604 2.1721 0.2200 0.9632
## site22R 0.5981 1.6720 0.2974 1.2028
## site23R 0.5453 1.8337 0.2637 1.1277
## site24R 0.6731 1.4856 0.3365 1.3464
## site25R 0.8564 1.1677 0.4390 1.6707
## site26R 0.4264 2.3451 0.1968 0.9239
## site27R 0.5834 1.7140 0.1238 2.7493
## site28R 0.4951 2.0199 0.1873 1.3083
## site29R 0.5226 1.9134 0.2113 1.2927
## site30R 0.5118 1.9539 0.2432 1.0772
## site31R 0.4843 2.0649 0.1891 1.2402
## site32R 0.6005 1.6654 0.2782 1.2963
## site33R 0.4502 2.2214 0.2114 0.9587
## site34R 0.5996 1.6678 0.3002 1.1974
## site35R 0.6816 1.4671 0.3378 1.3754
## site36R 0.4722 2.1179 0.2123 1.0499
## site37R 0.5033 1.9867 0.2321 1.0915
## site38R 0.4947 2.0214 0.2374 1.0307
## site39R 0.7361 1.3584 0.3543 1.5296
## site40R 0.8626 1.1592 0.4168 1.7854
## site41R 0.5653 1.7690 0.2579 1.2390
## site42R 0.6488 1.5414 0.2896 1.4532
##
## Concordance= 0.592 (se = 0.009 )
## Likelihood ratio test= 117.7 on 47 df, p=5e-08
## Wald test = 116.8 on 47 df, p=7e-08
## Score (logrank) test = 120.6 on 47 df, p=2e-08
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ SO4 + dx_yr + site, data=CARE_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SO4 + dx_yr +
## site, data = CARE_IPF, id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SO4 -1.23596 0.29056 0.23269 -5.312 1.09e-07 ***
## dx_yr 0.73332 2.08198 0.03528 20.784 < 2e-16 ***
## site102 -0.60491 0.54612 0.21057 -2.873 0.004069 **
## site103 -0.33396 0.71608 0.19653 -1.699 0.089272 .
## site104 -0.49248 0.61111 0.21087 -2.335 0.019521 *
## site105 -0.11901 0.88780 0.12965 -0.918 0.358663
## site106 0.35930 1.43233 0.12829 2.801 0.005098 **
## site107 -0.04064 0.96017 0.19854 -0.205 0.837800
## site108 -0.98954 0.37175 0.28413 -3.483 0.000496 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SO4 0.2906 3.4417 0.1841 0.4585
## dx_yr 2.0820 0.4803 1.9429 2.2310
## site102 0.5461 1.8311 0.3615 0.8251
## site103 0.7161 1.3965 0.4872 1.0526
## site104 0.6111 1.6364 0.4042 0.9239
## site105 0.8878 1.1264 0.6886 1.1447
## site106 1.4323 0.6982 1.1139 1.8418
## site107 0.9602 1.0415 0.6507 1.4169
## site108 0.3717 2.6900 0.2130 0.6488
##
## Concordance= 0.776 (se = 0.01 )
## Likelihood ratio test= 1079 on 9 df, p=<2e-16
## Wald test = 691.5 on 9 df, p=<2e-16
## Score (logrank) test = 586.5 on 9 df, p=<2e-16
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ SO4 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=CARE_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SO4 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = CARE_IPF,
## id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SO4 -1.286752 0.276166 0.238394 -5.398 6.75e-08 ***
## dx_yr 0.729942 2.074960 0.035557 20.529 < 2e-16 ***
## age_dx 0.012920 1.013004 0.004556 2.836 0.004570 **
## sexF -0.046963 0.954123 0.077139 -0.609 0.542653
## dich_RaceNon-White -0.123100 0.884176 0.110647 -1.113 0.265906
## smokeHxFormer -0.022266 0.977980 0.081559 -0.273 0.784853
## smokeHxAlways 0.078855 1.082047 0.168784 0.467 0.640363
## smokeHxUnknown 0.418192 1.519213 0.734668 0.569 0.569203
## disadv 0.038522 1.039273 0.130494 0.295 0.767841
## site102 -0.653165 0.520396 0.214710 -3.042 0.002349 **
## site103 -0.365703 0.693709 0.203983 -1.793 0.073003 .
## site104 -0.524951 0.591584 0.215369 -2.437 0.014791 *
## site105 -0.113263 0.892916 0.133202 -0.850 0.395154
## site106 0.333095 1.395280 0.129390 2.574 0.010043 *
## site107 -0.123085 0.884188 0.206526 -0.596 0.551188
## site108 -1.027788 0.357797 0.290655 -3.536 0.000406 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SO4 0.2762 3.6210 0.1731 0.4406
## dx_yr 2.0750 0.4819 1.9353 2.2247
## age_dx 1.0130 0.9872 1.0040 1.0221
## sexF 0.9541 1.0481 0.8202 1.1099
## dich_RaceNon-White 0.8842 1.1310 0.7118 1.0983
## smokeHxFormer 0.9780 1.0225 0.8335 1.1475
## smokeHxAlways 1.0820 0.9242 0.7773 1.5063
## smokeHxUnknown 1.5192 0.6582 0.3600 6.4116
## disadv 1.0393 0.9622 0.8047 1.3422
## site102 0.5204 1.9216 0.3416 0.7927
## site103 0.6937 1.4415 0.4651 1.0347
## site104 0.5916 1.6904 0.3879 0.9023
## site105 0.8929 1.1199 0.6877 1.1593
## site106 1.3953 0.7167 1.0827 1.7980
## site107 0.8842 1.1310 0.5899 1.3254
## site108 0.3578 2.7949 0.2024 0.6325
##
## Concordance= 0.778 (se = 0.01 )
## Likelihood ratio test= 1091 on 16 df, p=<2e-16
## Wald test = 697.8 on 16 df, p=<2e-16
## Score (logrank) test = 593.6 on 16 df, p=<2e-16
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ SO4 + dx_yr + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SO4 + dx_yr +
## site, data = All_IPF, id = ID, cluster = cohort)
##
## n= 123367, number of events= 2749
## (723 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## SO4 0.5110164 1.6669846 0.0377569 0.1279360 3.994 6.49e-05 ***
## dx_yr 0.2092500 1.2327532 0.0107065 0.0858800 2.437 0.014829 *
## site02R 0.3812251 1.4640772 0.3348219 0.0862520 4.420 9.87e-06 ***
## site03R -0.3712861 0.6898466 0.3471583 0.0615363 -6.034 1.60e-09 ***
## site04R -0.8659004 0.4206726 0.4517212 0.1965069 -4.406 1.05e-05 ***
## site05R -1.2586311 0.2840426 0.3746006 0.1977626 -6.364 1.96e-10 ***
## site06R -0.7494408 0.4726308 0.3289288 0.0904956 -8.282 < 2e-16 ***
## site07R -0.9821490 0.3745054 0.3084016 0.2222340 -4.419 9.90e-06 ***
## site09R -0.5021582 0.6052230 0.3413533 0.0974425 -5.153 2.56e-07 ***
## site1 -0.7787003 0.4590022 0.2917903 0.1241201 -6.274 3.52e-10 ***
## site101 -0.6661744 0.5136699 0.2959348 0.2197325 -3.032 0.002431 **
## site102 -0.3388445 0.7125933 0.2980522 0.1039720 -3.259 0.001118 **
## site103 -0.0005837 0.9994164 0.2863842 0.0789955 -0.007 0.994104
## site104 -0.3955723 0.6732946 0.2962273 0.1507913 -2.623 0.008708 **
## site105 -0.4105963 0.6632546 0.2916414 0.2037903 -2.015 0.043926 *
## site106 -0.8462814 0.4290073 0.2899438 0.2093542 -4.042 5.29e-05 ***
## site107 -0.3112724 0.7325143 0.3287640 0.2092562 -1.488 0.136878
## site108 -0.2267834 0.7970934 0.3702775 0.2129065 -1.065 0.286795
## site10R -0.5245681 0.5918109 0.3860936 0.1281927 -4.092 4.28e-05 ***
## site11R -0.7874952 0.4549830 0.3038425 0.1900834 -4.143 3.43e-05 ***
## site12R -0.3891524 0.6776310 0.3345130 0.0500968 -7.768 7.97e-15 ***
## site13R -1.0086030 0.3647282 0.3059838 0.2181510 -4.623 3.77e-06 ***
## site14R -0.9941338 0.3700439 0.6424121 0.0457805 -21.715 < 2e-16 ***
## site15R -0.5780147 0.5610111 0.3332739 0.1279243 -4.518 6.23e-06 ***
## site16R -0.3702838 0.6905383 0.3462580 0.0665255 -5.566 2.61e-08 ***
## site17R -0.6885830 0.5022873 0.3637417 0.1513004 -4.551 5.34e-06 ***
## site18R -0.9765812 0.3765964 0.3256429 0.2708528 -3.606 0.000311 ***
## site19R -0.0973785 0.9072125 0.3499296 0.0233002 -4.179 2.92e-05 ***
## site20R -0.7847289 0.4562434 0.3490013 0.1174909 -6.679 2.40e-11 ***
## site21R -1.0384796 0.3539925 0.3235616 0.1861927 -5.577 2.44e-08 ***
## site22R -0.7158559 0.4887736 0.3085409 0.1307190 -5.476 4.34e-08 ***
## site23R -0.7858803 0.4557184 0.3254688 0.1417148 -5.546 2.93e-08 ***
## site24R -0.4604258 0.6310149 0.3313096 0.0448845 -10.258 < 2e-16 ***
## site25R -0.2224960 0.8005182 0.3252118 0.0831548 -2.676 0.007458 **
## site26R -1.1199960 0.3262811 0.3483931 0.2386110 -4.694 2.68e-06 ***
## site27R -0.7885837 0.4544880 0.7616707 0.2495674 -3.160 0.001579 **
## site28R -1.0307198 0.3567501 0.4383679 0.1840166 -5.601 2.13e-08 ***
## site29R -0.8190257 0.4408610 0.4352044 0.1392063 -5.884 4.02e-09 ***
## site30R -0.9102542 0.4024219 0.3342595 0.2279192 -3.994 6.50e-05 ***
## site31R -0.9261408 0.3960793 0.4513755 0.1716932 -5.394 6.88e-08 ***
## site32R -0.4981559 0.6076502 0.3686632 0.0676925 -7.359 1.85e-13 ***
## site33R -1.0704910 0.3428401 0.3362189 0.2328277 -4.598 4.27e-06 ***
## site34R -0.6904456 0.5013526 0.3156926 0.1471800 -4.691 2.72e-06 ***
## site35R -0.4596752 0.6314887 0.3346422 0.0903721 -5.086 3.65e-07 ***
## site36R -1.0548550 0.3482429 0.3429184 0.1792733 -5.884 4.00e-09 ***
## site37R -0.9660688 0.3805762 0.3385391 0.1918401 -5.036 4.76e-07 ***
## site38R -0.9170174 0.3997094 0.3319008 0.1435756 -6.387 1.69e-10 ***
## site39R -0.2879558 0.7497948 0.3600645 0.0295062 -9.759 < 2e-16 ***
## site40R -0.3403622 0.7115126 0.3440754 0.1769919 -1.923 0.054475 .
## site41R -0.7468242 0.4738691 0.3704045 0.1329629 -5.617 1.95e-08 ***
## site42R -0.6534198 0.5202635 0.3637355 0.1597664 -4.090 4.32e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SO4 1.6670 0.5999 1.2973 2.1421
## dx_yr 1.2328 0.8112 1.0418 1.4587
## site02R 1.4641 0.6830 1.2364 1.7337
## site03R 0.6898 1.4496 0.6115 0.7783
## site04R 0.4207 2.3771 0.2862 0.6183
## site05R 0.2840 3.5206 0.1928 0.4185
## site06R 0.4726 2.1158 0.3958 0.5644
## site07R 0.3745 2.6702 0.2423 0.5789
## site09R 0.6052 1.6523 0.5000 0.7326
## site1 0.4590 2.1786 0.3599 0.5854
## site101 0.5137 1.9468 0.3339 0.7902
## site102 0.7126 1.4033 0.5812 0.8737
## site103 0.9994 1.0006 0.8561 1.1668
## site104 0.6733 1.4852 0.5010 0.9048
## site105 0.6633 1.5077 0.4449 0.9889
## site106 0.4290 2.3310 0.2846 0.6466
## site107 0.7325 1.3652 0.4861 1.1039
## site108 0.7971 1.2546 0.5251 1.2099
## site10R 0.5918 1.6897 0.4603 0.7609
## site11R 0.4550 2.1979 0.3135 0.6604
## site12R 0.6776 1.4757 0.6143 0.7475
## site13R 0.3647 2.7418 0.2378 0.5593
## site14R 0.3700 2.7024 0.3383 0.4048
## site15R 0.5610 1.7825 0.4366 0.7209
## site16R 0.6905 1.4481 0.6061 0.7867
## site17R 0.5023 1.9909 0.3734 0.6757
## site18R 0.3766 2.6554 0.2215 0.6404
## site19R 0.9072 1.1023 0.8667 0.9496
## site20R 0.4562 2.1918 0.3624 0.5744
## site21R 0.3540 2.8249 0.2458 0.5099
## site22R 0.4888 2.0459 0.3783 0.6315
## site23R 0.4557 2.1943 0.3452 0.6016
## site24R 0.6310 1.5847 0.5779 0.6890
## site25R 0.8005 1.2492 0.6801 0.9422
## site26R 0.3263 3.0648 0.2044 0.5208
## site27R 0.4545 2.2003 0.2787 0.7412
## site28R 0.3568 2.8031 0.2487 0.5117
## site29R 0.4409 2.2683 0.3356 0.5792
## site30R 0.4024 2.4850 0.2574 0.6291
## site31R 0.3961 2.5247 0.2829 0.5545
## site32R 0.6077 1.6457 0.5321 0.6939
## site33R 0.3428 2.9168 0.2172 0.5411
## site34R 0.5014 1.9946 0.3757 0.6690
## site35R 0.6315 1.5836 0.5290 0.7539
## site36R 0.3482 2.8716 0.2451 0.4949
## site37R 0.3806 2.6276 0.2613 0.5543
## site38R 0.3997 2.5018 0.3017 0.5296
## site39R 0.7498 1.3337 0.7077 0.7944
## site40R 0.7115 1.4055 0.5030 1.0066
## site41R 0.4739 2.1103 0.3652 0.6149
## site42R 0.5203 1.9221 0.3804 0.7116
##
## Concordance= 0.612 (se = 0.044 )
## Likelihood ratio test= 492.6 on 51 df, p=<2e-16
## Wald test = 116 on 51 df, p=6e-07
## Score (logrank) test = 458.8 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ SO4 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SO4 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All_IPF,
## id = ID, cluster = cohort)
##
## n= 121241, number of events= 2709
## (2849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## SO4 0.520926 1.683587 0.038357 0.126883 4.106 4.03e-05 ***
## dx_yr 0.208219 1.231483 0.011049 0.089361 2.330 0.019802 *
## age_dx 0.002714 1.002717 0.002440 0.001988 1.365 0.172146
## sexF -0.142260 0.867396 0.044898 0.087104 -1.633 0.102423
## dich_RaceNon-White 0.026394 1.026746 0.069038 0.077443 0.341 0.733237
## smokeHxFormer 0.122602 1.130435 0.059501 0.012929 9.483 < 2e-16 ***
## smokeHxAlways 0.003729 1.003736 0.138752 0.117842 0.032 0.974754
## smokeHxUnknown 0.337323 1.401192 0.153191 0.133651 2.524 0.011606 *
## smokeHxEver 0.033698 1.034272 0.064183 0.003070 10.977 < 2e-16 ***
## disadv 0.050061 1.051335 0.070820 0.127129 0.394 0.693743
## site02R 0.358416 1.431061 0.345503 0.098292 3.646 0.000266 ***
## site03R -0.359633 0.697933 0.357528 0.074615 -4.820 1.44e-06 ***
## site04R -0.876248 0.416342 0.460091 0.212618 -4.121 3.77e-05 ***
## site05R -1.280350 0.277940 0.385123 0.193983 -6.600 4.10e-11 ***
## site06R -0.783987 0.456582 0.341869 0.058329 -13.441 < 2e-16 ***
## site07R -1.007042 0.365298 0.320141 0.217857 -4.623 3.79e-06 ***
## site09R -0.558600 0.572009 0.353638 0.091666 -6.094 1.10e-09 ***
## site1 -0.890842 0.410310 0.309142 0.109898 -8.106 5.23e-16 ***
## site101 -0.751699 0.471565 0.311617 0.233845 -3.215 0.001307 **
## site102 -0.433928 0.647959 0.314090 0.137654 -3.152 0.001620 **
## site103 -0.104103 0.901132 0.302191 0.077670 -1.340 0.180139
## site104 -0.489837 0.612726 0.311985 0.152522 -3.212 0.001320 **
## site105 -0.507254 0.602147 0.309478 0.217336 -2.334 0.019598 *
## site106 -0.958363 0.383520 0.306594 0.222293 -4.311 1.62e-05 ***
## site107 -0.435567 0.646898 0.344920 0.221396 -1.967 0.049141 *
## site108 -0.330708 0.718415 0.383011 0.225210 -1.468 0.141984
## site10R -0.592983 0.552676 0.396830 0.112457 -5.273 1.34e-07 ***
## site11R -0.843849 0.430052 0.318160 0.179497 -4.701 2.59e-06 ***
## site12R -0.422370 0.655492 0.345748 0.040390 -10.457 < 2e-16 ***
## site13R -1.060599 0.346248 0.317541 0.218003 -4.865 1.14e-06 ***
## site14R -1.018665 0.361077 0.648296 0.041257 -24.691 < 2e-16 ***
## site15R -0.635002 0.529934 0.345472 0.106049 -5.988 2.13e-09 ***
## site16R -0.390440 0.676759 0.356497 0.052564 -7.428 1.10e-13 ***
## site17R -0.725185 0.484235 0.374751 0.144867 -5.006 5.56e-07 ***
## site18R -1.025649 0.358564 0.337674 0.246077 -4.168 3.07e-05 ***
## site19R -0.144486 0.865467 0.360587 0.014129 -10.226 < 2e-16 ***
## site20R -0.800300 0.449194 0.360432 0.119093 -6.720 1.82e-11 ***
## site21R -1.091467 0.335724 0.334911 0.174087 -6.270 3.62e-10 ***
## site22R -0.743512 0.475441 0.320519 0.134476 -5.529 3.22e-08 ***
## site23R -0.857260 0.424323 0.339231 0.120987 -7.086 1.39e-12 ***
## site24R -0.480789 0.618295 0.342961 0.032167 -14.947 < 2e-16 ***
## site25R -0.252844 0.776589 0.336897 0.101385 -2.494 0.012635 *
## site26R -1.177002 0.308201 0.359258 0.215928 -5.451 5.01e-08 ***
## site27R -0.806733 0.446314 0.769289 0.245361 -3.288 0.001009 **
## site28R -1.053512 0.348711 0.447268 0.170000 -6.197 5.75e-10 ***
## site29R -0.833342 0.434595 0.444429 0.175186 -4.757 1.97e-06 ***
## site30R -0.986645 0.372826 0.346247 0.223610 -4.412 1.02e-05 ***
## site31R -0.974724 0.377297 0.459506 0.164924 -5.910 3.42e-09 ***
## site32R -0.574440 0.563020 0.388897 0.069742 -8.237 < 2e-16 ***
## site33R -1.110989 0.329233 0.349163 0.205275 -5.412 6.23e-08 ***
## site34R -0.715291 0.489050 0.328338 0.150969 -4.738 2.16e-06 ***
## site35R -0.518945 0.595148 0.347588 0.081002 -6.407 1.49e-10 ***
## site36R -1.080404 0.339458 0.354157 0.170323 -6.343 2.25e-10 ***
## site37R -0.999994 0.367881 0.349418 0.196010 -5.102 3.37e-07 ***
## site38R -0.944552 0.388854 0.343268 0.120605 -7.832 4.81e-15 ***
## site39R -0.330590 0.718500 0.370684 0.020696 -15.973 < 2e-16 ***
## site40R -0.400477 0.670001 0.354043 0.152032 -2.634 0.008435 **
## site41R -0.800329 0.449181 0.379676 0.121612 -6.581 4.67e-11 ***
## site42R -0.695337 0.498906 0.373927 0.159468 -4.360 1.30e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SO4 1.6836 0.5940 1.3129 2.1589
## dx_yr 1.2315 0.8120 1.0336 1.4672
## age_dx 1.0027 0.9973 0.9988 1.0066
## sexF 0.8674 1.1529 0.7313 1.0289
## dich_RaceNon-White 1.0267 0.9740 0.8822 1.1950
## smokeHxFormer 1.1304 0.8846 1.1021 1.1594
## smokeHxAlways 1.0037 0.9963 0.7967 1.2645
## smokeHxUnknown 1.4012 0.7137 1.0783 1.8208
## smokeHxEver 1.0343 0.9669 1.0281 1.0405
## disadv 1.0513 0.9512 0.8195 1.3488
## site02R 1.4311 0.6988 1.1803 1.7351
## site03R 0.6979 1.4328 0.6030 0.8078
## site04R 0.4163 2.4019 0.2745 0.6316
## site05R 0.2779 3.5979 0.1900 0.4065
## site06R 0.4566 2.1902 0.4073 0.5119
## site07R 0.3653 2.7375 0.2383 0.5599
## site09R 0.5720 1.7482 0.4779 0.6846
## site1 0.4103 2.4372 0.3308 0.5089
## site101 0.4716 2.1206 0.2982 0.7457
## site102 0.6480 1.5433 0.4947 0.8486
## site103 0.9011 1.1097 0.7739 1.0493
## site104 0.6127 1.6321 0.4544 0.8262
## site105 0.6021 1.6607 0.3933 0.9219
## site106 0.3835 2.6074 0.2481 0.5929
## site107 0.6469 1.5458 0.4192 0.9984
## site108 0.7184 1.3920 0.4620 1.1171
## site10R 0.5527 1.8094 0.4434 0.6890
## site11R 0.4301 2.3253 0.3025 0.6114
## site12R 0.6555 1.5256 0.6056 0.7095
## site13R 0.3462 2.8881 0.2259 0.5308
## site14R 0.3611 2.7695 0.3330 0.3915
## site15R 0.5299 1.8870 0.4305 0.6524
## site16R 0.6768 1.4776 0.6105 0.7502
## site17R 0.4842 2.0651 0.3645 0.6432
## site18R 0.3586 2.7889 0.2214 0.5808
## site19R 0.8655 1.1554 0.8418 0.8898
## site20R 0.4492 2.2262 0.3557 0.5673
## site21R 0.3357 2.9786 0.2387 0.4722
## site22R 0.4754 2.1033 0.3653 0.6188
## site23R 0.4243 2.3567 0.3347 0.5379
## site24R 0.6183 1.6173 0.5805 0.6585
## site25R 0.7766 1.2877 0.6366 0.9473
## site26R 0.3082 3.2446 0.2019 0.4706
## site27R 0.4463 2.2406 0.2759 0.7219
## site28R 0.3487 2.8677 0.2499 0.4866
## site29R 0.4346 2.3010 0.3083 0.6126
## site30R 0.3728 2.6822 0.2405 0.5779
## site31R 0.3773 2.6504 0.2731 0.5213
## site32R 0.5630 1.7761 0.4911 0.6455
## site33R 0.3292 3.0374 0.2202 0.4923
## site34R 0.4890 2.0448 0.3638 0.6574
## site35R 0.5951 1.6803 0.5078 0.6975
## site36R 0.3395 2.9459 0.2431 0.4740
## site37R 0.3679 2.7183 0.2505 0.5402
## site38R 0.3889 2.5717 0.3070 0.4925
## site39R 0.7185 1.3918 0.6899 0.7482
## site40R 0.6700 1.4925 0.4974 0.9026
## site41R 0.4492 2.2263 0.3539 0.5701
## site42R 0.4989 2.0044 0.3650 0.6820
##
## Concordance= 0.617 (se = 0.041 )
## Likelihood ratio test= 525.5 on 59 df, p=<2e-16
## Wald test = 130.7 on 59 df, p=2e-07
## Score (logrank) test = 494.4 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ NO3 + dx_yr, data=Simm_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NO3 + dx_yr, data = Simm_IPF,
## id = ID)
##
## n= 31982, number of events= 695
## (446 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## NO3 0.078119 1.081251 0.120972 0.646 0.51844
## dx_yr 0.025192 1.025512 0.008946 2.816 0.00486 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NO3 1.081 0.9249 0.853 1.371
## dx_yr 1.026 0.9751 1.008 1.044
##
## Concordance= 0.523 (se = 0.013 )
## Likelihood ratio test= 8 on 2 df, p=0.02
## Wald test = 7.97 on 2 df, p=0.02
## Score (logrank) test = 7.99 on 2 df, p=0.02
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ NO3 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv, data=Simm_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NO3 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv, data = Simm_IPF,
## id = ID)
##
## n= 30488, number of events= 670
## (1940 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## NO3 0.065691 1.067897 0.126750 0.518 0.60427
## dx_yr 0.012666 1.012746 0.009522 1.330 0.18348
## age_dx 0.002397 1.002400 0.004560 0.526 0.59908
## sexF -0.377364 0.685666 0.089126 -4.234 2.3e-05 ***
## dich_RaceNon-White 0.152740 1.165022 0.128781 1.186 0.23561
## smokeHxFormer 0.087396 1.091329 0.091830 0.952 0.34124
## smokeHxAlways -0.407825 0.665095 0.266536 -1.530 0.12599
## smokeHxUnknown 0.508275 1.662421 0.169685 2.995 0.00274 **
## disadv 0.381580 1.464597 0.134657 2.834 0.00460 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NO3 1.0679 0.9364 0.8330 1.3690
## dx_yr 1.0127 0.9874 0.9940 1.0318
## age_dx 1.0024 0.9976 0.9935 1.0114
## sexF 0.6857 1.4584 0.5758 0.8165
## dich_RaceNon-White 1.1650 0.8584 0.9051 1.4995
## smokeHxFormer 1.0913 0.9163 0.9116 1.3065
## smokeHxAlways 0.6651 1.5035 0.3945 1.1214
## smokeHxUnknown 1.6624 0.6015 1.1921 2.3183
## disadv 1.4646 0.6828 1.1249 1.9069
##
## Concordance= 0.591 (se = 0.012 )
## Likelihood ratio test= 51.89 on 9 df, p=5e-08
## Wald test = 52.36 on 9 df, p=4e-08
## Score (logrank) test = 53.1 on 9 df, p=3e-08
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ NO3 + dx_yr + site, data=PFF_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NO3 + dx_yr +
## site, data = PFF_IPF, id = ID)
##
## n= 54499, number of events= 1146
## (217 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## NO3 -0.10347 0.90170 0.09648 -1.072 0.284
## dx_yr 0.10001 1.10518 0.01409 7.096 1.28e-12 ***
## site02R 0.75122 2.11958 0.34621 2.170 0.030 *
## site03R -0.21561 0.80605 0.34813 -0.619 0.536
## site04R -0.06236 0.93954 0.46930 -0.133 0.894
## site05R -0.21424 0.80715 0.37696 -0.568 0.570
## site06R 0.00428 1.00429 0.32606 0.013 0.990
## site07R -0.19408 0.82359 0.32121 -0.604 0.546
## site08R NA NA 0.00000 NA NA
## site09R 0.03011 1.03057 0.34203 0.088 0.930
## site10R -0.15118 0.85969 0.38536 -0.392 0.695
## site11R -0.05390 0.94753 0.29955 -0.180 0.857
## site12R 0.00305 1.00306 0.34261 0.009 0.993
## site13R -0.23298 0.79217 0.30593 -0.762 0.446
## site14R -0.26079 0.77044 0.64636 -0.403 0.687
## site15R 0.03762 1.03834 0.33050 0.114 0.909
## site16R 0.20490 1.22740 0.34915 0.587 0.557
## site17R 0.13644 1.14618 0.37027 0.368 0.713
## site18R -0.25622 0.77398 0.32260 -0.794 0.427
## site19R -0.02996 0.97049 0.35044 -0.085 0.932
## site20R -0.16763 0.84567 0.35413 -0.473 0.636
## site21R -0.26543 0.76687 0.31967 -0.830 0.406
## site22R 0.03373 1.03430 0.31570 0.107 0.915
## site23R -0.11986 0.88704 0.32376 -0.370 0.711
## site24R -0.03264 0.96788 0.34420 -0.095 0.924
## site25R 0.08598 1.08978 0.33518 0.257 0.798
## site26R -0.38737 0.67884 0.34465 -1.124 0.261
## site27R 0.10566 1.11144 0.76372 0.138 0.890
## site28R -0.01218 0.98789 0.44320 -0.027 0.978
## site29R -0.23190 0.79302 0.43549 -0.533 0.594
## site30R -0.04987 0.95135 0.35833 -0.139 0.889
## site31R -0.29876 0.74174 0.44961 -0.664 0.506
## site32R -0.32873 0.71984 0.36871 -0.892 0.373
## site33R -0.31342 0.73095 0.33261 -0.942 0.346
## site34R -0.09133 0.91272 0.31636 -0.289 0.773
## site35R -0.09434 0.90997 0.33396 -0.282 0.778
## site36R -0.08052 0.92263 0.35260 -0.228 0.819
## site37R -0.05496 0.94652 0.34745 -0.158 0.874
## site38R -0.26833 0.76465 0.32907 -0.815 0.415
## site39R -0.13661 0.87231 0.36429 -0.375 0.708
## site40R 0.38370 1.46770 0.39266 0.977 0.328
## site41R -0.12718 0.88058 0.37427 -0.340 0.734
## site42R 0.15895 1.17228 0.36632 0.434 0.664
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NO3 0.9017 1.1090 0.7463 1.089
## dx_yr 1.1052 0.9048 1.0751 1.136
## site02R 2.1196 0.4718 1.0754 4.178
## site03R 0.8060 1.2406 0.4074 1.595
## site04R 0.9395 1.0643 0.3745 2.357
## site05R 0.8072 1.2389 0.3856 1.690
## site06R 1.0043 0.9957 0.5300 1.903
## site07R 0.8236 1.2142 0.4388 1.546
## site08R NA NA NA NA
## site09R 1.0306 0.9703 0.5272 2.015
## site10R 0.8597 1.1632 0.4039 1.830
## site11R 0.9475 1.0554 0.5268 1.704
## site12R 1.0031 0.9970 0.5125 1.963
## site13R 0.7922 1.2624 0.4349 1.443
## site14R 0.7704 1.2980 0.2170 2.735
## site15R 1.0383 0.9631 0.5433 1.985
## site16R 1.2274 0.8147 0.6191 2.433
## site17R 1.1462 0.8725 0.5547 2.368
## site18R 0.7740 1.2920 0.4113 1.457
## site19R 0.9705 1.0304 0.4883 1.929
## site20R 0.8457 1.1825 0.4224 1.693
## site21R 0.7669 1.3040 0.4098 1.435
## site22R 1.0343 0.9668 0.5571 1.920
## site23R 0.8870 1.1273 0.4703 1.673
## site24R 0.9679 1.0332 0.4930 1.900
## site25R 1.0898 0.9176 0.5650 2.102
## site26R 0.6788 1.4731 0.3455 1.334
## site27R 1.1114 0.8997 0.2488 4.966
## site28R 0.9879 1.0123 0.4144 2.355
## site29R 0.7930 1.2610 0.3377 1.862
## site30R 0.9514 1.0511 0.4713 1.920
## site31R 0.7417 1.3482 0.3073 1.790
## site32R 0.7198 1.3892 0.3495 1.483
## site33R 0.7309 1.3681 0.3809 1.403
## site34R 0.9127 1.0956 0.4910 1.697
## site35R 0.9100 1.0989 0.4729 1.751
## site36R 0.9226 1.0839 0.4623 1.841
## site37R 0.9465 1.0565 0.4790 1.870
## site38R 0.7647 1.3078 0.4012 1.457
## site39R 0.8723 1.1464 0.4272 1.781
## site40R 1.4677 0.6813 0.6798 3.169
## site41R 0.8806 1.1356 0.4229 1.834
## site42R 1.1723 0.8530 0.5718 2.403
##
## Concordance= 0.589 (se = 0.009 )
## Likelihood ratio test= 111.7 on 42 df, p=3e-08
## Wald test = 110 on 42 df, p=5e-08
## Score (logrank) test = 114.3 on 42 df, p=1e-08
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ NO3 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=PFF_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NO3 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = PFF_IPF,
## id = ID)
##
## n= 53867, number of events= 1131
## (849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## NO3 -0.0996736 0.9051328 0.0977188 -1.020 0.3077
## dx_yr 0.0986732 1.1037056 0.0145693 6.773 1.26e-11 ***
## age_dx -0.0001214 0.9998786 0.0039361 -0.031 0.9754
## sexM 0.0638078 1.0658875 0.0713192 0.895 0.3710
## dich_RaceNon-White -0.0519724 0.9493550 0.1259445 -0.413 0.6799
## smokeHxEver 0.0445620 1.0455698 0.0646169 0.690 0.4904
## disadv -0.0544620 0.9469945 0.1122059 -0.485 0.6274
## site02R 0.7071011 2.0281035 0.3589002 1.970 0.0488 *
## site03R -0.2374812 0.7886117 0.3604155 -0.659 0.5100
## site04R -0.1124773 0.8936177 0.4804784 -0.234 0.8149
## site05R -0.2524409 0.7769022 0.3894613 -0.648 0.5169
## site06R -0.0238829 0.9764001 0.3415139 -0.070 0.9442
## site07R -0.2316936 0.7931891 0.3357447 -0.690 0.4901
## site08R NA NA 0.0000000 NA NA
## site09R -0.0441533 0.9568073 0.3563955 -0.124 0.9014
## site10R -0.2001765 0.8185862 0.3984932 -0.502 0.6154
## site11R -0.1005138 0.9043727 0.3170201 -0.317 0.7512
## site12R -0.0352559 0.9653583 0.3570301 -0.099 0.9213
## site13R -0.2837648 0.7529437 0.3191352 -0.889 0.3739
## site14R -0.3154306 0.7294747 0.6538639 -0.482 0.6295
## site15R -0.0010874 0.9989132 0.3455663 -0.003 0.9975
## site16R 0.1658084 1.1803469 0.3606762 0.460 0.6457
## site17R 0.0857695 1.0895551 0.3840186 0.223 0.8233
## site18R -0.2810226 0.7550113 0.3376581 -0.832 0.4053
## site19R -0.0683353 0.9339473 0.3626204 -0.188 0.8505
## site20R -0.2127411 0.8083654 0.3682848 -0.578 0.5635
## site21R -0.3003307 0.7405732 0.3329195 -0.902 0.3670
## site22R -0.0136384 0.9864541 0.3300969 -0.041 0.9670
## site23R -0.1750541 0.8394116 0.3393329 -0.516 0.6059
## site24R -0.0834380 0.9199481 0.3584811 -0.233 0.8160
## site25R 0.0582457 1.0599754 0.3484858 0.167 0.8673
## site26R -0.4129137 0.6617194 0.3574071 -1.155 0.2480
## site27R 0.0072336 1.0072599 0.7748598 0.009 0.9926
## site28R -0.0362095 0.9644383 0.4551403 -0.080 0.9366
## site29R -0.2788161 0.7566790 0.4468028 -0.624 0.5326
## site30R -0.1004341 0.9044447 0.3723484 -0.270 0.7874
## site31R -0.3421022 0.7102756 0.4584973 -0.746 0.4556
## site32R -0.3742243 0.6878226 0.3905748 -0.958 0.3380
## site33R -0.3481023 0.7060266 0.3479638 -1.000 0.3171
## site34R -0.1274019 0.8803797 0.3317365 -0.384 0.7009
## site35R -0.1466239 0.8636187 0.3481678 -0.421 0.6737
## site36R -0.1066625 0.8988290 0.3675072 -0.290 0.7716
## site37R -0.0934345 0.9107977 0.3605854 -0.259 0.7955
## site38R -0.2970943 0.7429739 0.3423768 -0.868 0.3855
## site39R -0.1815916 0.8339419 0.3769172 -0.482 0.6300
## site40R 0.3515743 1.4213033 0.4044017 0.869 0.3846
## site41R -0.1603955 0.8518068 0.3847990 -0.417 0.6768
## site42R 0.0993722 1.1044773 0.3784940 0.263 0.7929
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NO3 0.9051 1.1048 0.7474 1.096
## dx_yr 1.1037 0.9060 1.0726 1.136
## age_dx 0.9999 1.0001 0.9922 1.008
## sexM 1.0659 0.9382 0.9268 1.226
## dich_RaceNon-White 0.9494 1.0533 0.7417 1.215
## smokeHxEver 1.0456 0.9564 0.9212 1.187
## disadv 0.9470 1.0560 0.7600 1.180
## site02R 2.0281 0.4931 1.0037 4.098
## site03R 0.7886 1.2681 0.3891 1.598
## site04R 0.8936 1.1190 0.3485 2.292
## site05R 0.7769 1.2872 0.3621 1.667
## site06R 0.9764 1.0242 0.5000 1.907
## site07R 0.7932 1.2607 0.4108 1.532
## site08R NA NA NA NA
## site09R 0.9568 1.0451 0.4758 1.924
## site10R 0.8186 1.2216 0.3749 1.788
## site11R 0.9044 1.1057 0.4858 1.683
## site12R 0.9654 1.0359 0.4795 1.944
## site13R 0.7529 1.3281 0.4028 1.407
## site14R 0.7295 1.3708 0.2025 2.628
## site15R 0.9989 1.0011 0.5074 1.966
## site16R 1.1803 0.8472 0.5821 2.393
## site17R 1.0896 0.9178 0.5133 2.313
## site18R 0.7550 1.3245 0.3895 1.463
## site19R 0.9339 1.0707 0.4588 1.901
## site20R 0.8084 1.2371 0.3928 1.664
## site21R 0.7406 1.3503 0.3856 1.422
## site22R 0.9865 1.0137 0.5165 1.884
## site23R 0.8394 1.1913 0.4317 1.632
## site24R 0.9199 1.0870 0.4556 1.857
## site25R 1.0600 0.9434 0.5354 2.099
## site26R 0.6617 1.5112 0.3284 1.333
## site27R 1.0073 0.9928 0.2206 4.599
## site28R 0.9644 1.0369 0.3952 2.353
## site29R 0.7567 1.3216 0.3152 1.816
## site30R 0.9044 1.1057 0.4360 1.876
## site31R 0.7103 1.4079 0.2892 1.745
## site32R 0.6878 1.4539 0.3199 1.479
## site33R 0.7060 1.4164 0.3570 1.396
## site34R 0.8804 1.1359 0.4595 1.687
## site35R 0.8636 1.1579 0.4365 1.709
## site36R 0.8988 1.1126 0.4374 1.847
## site37R 0.9108 1.0979 0.4493 1.847
## site38R 0.7430 1.3459 0.3798 1.453
## site39R 0.8339 1.1991 0.3984 1.746
## site40R 1.4213 0.7036 0.6434 3.140
## site41R 0.8518 1.1740 0.4007 1.811
## site42R 1.1045 0.9054 0.5260 2.319
##
## Concordance= 0.591 (se = 0.009 )
## Likelihood ratio test= 112.2 on 47 df, p=3e-07
## Wald test = 110.8 on 47 df, p=4e-07
## Score (logrank) test = 115.1 on 47 df, p=1e-07
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ NO3 + dx_yr + site, data=CARE_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NO3 + dx_yr +
## site, data = CARE_IPF, id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## NO3 -0.270478 0.763014 0.196328 -1.378 0.1683
## dx_yr 0.808432 2.244386 0.032614 24.788 <2e-16 ***
## site102 0.118986 1.126354 0.164897 0.722 0.4706
## site103 0.372193 1.450913 0.160649 2.317 0.0205 *
## site104 0.227337 1.255252 0.174687 1.301 0.1931
## site105 0.006732 1.006755 0.133282 0.051 0.9597
## site106 0.167708 1.182592 0.123642 1.356 0.1750
## site107 0.052914 1.054339 0.201123 0.263 0.7925
## site108 -0.449046 0.638236 0.266256 -1.687 0.0917 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NO3 0.7630 1.3106 0.5193 1.121
## dx_yr 2.2444 0.4456 2.1054 2.393
## site102 1.1264 0.8878 0.8153 1.556
## site103 1.4509 0.6892 1.0590 1.988
## site104 1.2553 0.7967 0.8913 1.768
## site105 1.0068 0.9933 0.7753 1.307
## site106 1.1826 0.8456 0.9281 1.507
## site107 1.0543 0.9485 0.7109 1.564
## site108 0.6382 1.5668 0.3787 1.076
##
## Concordance= 0.774 (se = 0.01 )
## Likelihood ratio test= 1054 on 9 df, p=<2e-16
## Wald test = 666.7 on 9 df, p=<2e-16
## Score (logrank) test = 586.4 on 9 df, p=<2e-16
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ NO3 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=CARE_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NO3 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = CARE_IPF,
## id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## NO3 -0.3037208 0.7380669 0.1996932 -1.521 0.12828
## dx_yr 0.8073192 2.2418900 0.0327554 24.647 < 2e-16 ***
## age_dx 0.0121849 1.0122595 0.0045119 2.701 0.00692 **
## sexF -0.0441648 0.9567963 0.0770348 -0.573 0.56644
## dich_RaceNon-White -0.1468558 0.8634185 0.1103880 -1.330 0.18340
## smokeHxFormer -0.0002003 0.9997998 0.0815445 -0.002 0.99804
## smokeHxAlways 0.0518455 1.0532130 0.1686644 0.307 0.75855
## smokeHxUnknown 0.4203794 1.5225390 0.7347411 0.572 0.56722
## disadv 0.0006266 1.0006268 0.1301703 0.005 0.99616
## site102 0.0888252 1.0928896 0.1668224 0.532 0.59441
## site103 0.3700427 1.4477964 0.1644577 2.250 0.02444 *
## site104 0.2179053 1.2434693 0.1769330 1.232 0.21811
## site105 0.0174679 1.0176214 0.1368009 0.128 0.89840
## site106 0.1401743 1.1504743 0.1247604 1.124 0.26120
## site107 -0.0165668 0.9835697 0.2090597 -0.079 0.93684
## site108 -0.4468570 0.6396354 0.2694624 -1.658 0.09725 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NO3 0.7381 1.3549 0.4990 1.092
## dx_yr 2.2419 0.4461 2.1025 2.391
## age_dx 1.0123 0.9879 1.0033 1.021
## sexF 0.9568 1.0452 0.8227 1.113
## dich_RaceNon-White 0.8634 1.1582 0.6954 1.072
## smokeHxFormer 0.9998 1.0002 0.8521 1.173
## smokeHxAlways 1.0532 0.9495 0.7567 1.466
## smokeHxUnknown 1.5225 0.6568 0.3607 6.427
## disadv 1.0006 0.9994 0.7753 1.291
## site102 1.0929 0.9150 0.7881 1.516
## site103 1.4478 0.6907 1.0489 1.998
## site104 1.2435 0.8042 0.8791 1.759
## site105 1.0176 0.9827 0.7783 1.331
## site106 1.1505 0.8692 0.9009 1.469
## site107 0.9836 1.0167 0.6529 1.482
## site108 0.6396 1.5634 0.3772 1.085
##
## Concordance= 0.776 (se = 0.01 )
## Likelihood ratio test= 1066 on 16 df, p=<2e-16
## Wald test = 673.8 on 16 df, p=<2e-16
## Score (logrank) test = 593.2 on 16 df, p=<2e-16
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ NO3 + dx_yr + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NO3 + dx_yr +
## site, data = All_IPF, id = ID, cluster = cohort)
##
## n= 123367, number of events= 2749
## (723 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## NO3 -0.055082 0.946407 0.071464 0.032913 -1.674 0.094211 .
## dx_yr 0.102461 1.107894 0.007228 0.075557 1.356 0.175076
## site02R 0.716687 2.047638 0.340074 0.075150 9.537 < 2e-16 ***
## site03R -0.257667 0.772852 0.347669 0.033202 -7.761 8.45e-15 ***
## site04R -0.149383 0.861239 0.460121 0.095850 -1.559 0.119113
## site05R -0.282614 0.753811 0.373167 0.044810 -6.307 2.85e-10 ***
## site06R -0.030122 0.970327 0.325474 0.073079 -0.412 0.680201
## site07R -0.265497 0.766825 0.313618 0.108258 -2.452 0.014189 *
## site09R -0.011993 0.988079 0.341013 0.030999 -0.387 0.698845
## site1 0.274407 1.315750 0.283710 0.107072 2.563 0.010382 *
## site101 -0.077716 0.925227 0.296019 0.141757 -0.548 0.583532
## site102 -0.226097 0.797641 0.298189 0.114292 -1.978 0.047903 *
## site103 0.094436 1.099039 0.286317 0.088962 1.062 0.288450
## site104 -0.190098 0.826878 0.294700 0.091467 -2.078 0.037680 *
## site105 0.104458 1.110109 0.290712 0.149457 0.699 0.484604
## site106 -0.167587 0.845703 0.291194 0.100077 -1.675 0.094015 .
## site107 0.234657 1.264475 0.327753 0.160000 1.467 0.142483
## site108 0.169447 1.184650 0.370441 0.207307 0.817 0.413715
## site10R -0.154385 0.856942 0.385246 0.071695 -2.153 0.031290 *
## site11R -0.058308 0.943360 0.299375 0.064010 -0.911 0.362340
## site12R -0.056247 0.945305 0.338793 0.043534 -1.292 0.196347
## site13R -0.270225 0.763208 0.303866 0.100445 -2.690 0.007139 **
## site14R -0.317286 0.728123 0.643776 0.123325 -2.573 0.010089 *
## site15R 0.031964 1.032481 0.330466 0.014503 2.204 0.027526 *
## site16R 0.174272 1.190379 0.347089 0.039028 4.465 8.00e-06 ***
## site17R 0.092022 1.096389 0.365664 0.029937 3.074 0.002113 **
## site18R -0.257438 0.773030 0.321665 0.159066 -1.618 0.105570
## site19R -0.042549 0.958343 0.350157 0.009774 -4.353 1.34e-05 ***
## site20R -0.232595 0.792474 0.350990 0.042607 -5.459 4.79e-08 ***
## site21R -0.304105 0.737783 0.319500 0.051740 -5.878 4.16e-09 ***
## site22R -0.023130 0.977136 0.311019 0.037807 -0.612 0.540683
## site23R -0.147031 0.863267 0.323173 0.024710 -5.950 2.68e-09 ***
## site24R -0.110673 0.895231 0.338152 0.066507 -1.664 0.096096 .
## site25R 0.026198 1.026544 0.330693 0.058690 0.446 0.655320
## site26R -0.410581 0.663265 0.344492 0.083157 -4.937 7.92e-07 ***
## site27R 0.027991 1.028387 0.761761 0.117887 0.237 0.812314
## site28R -0.088808 0.915021 0.438947 0.050031 -1.775 0.075888 .
## site29R -0.283223 0.753352 0.434674 0.053451 -5.299 1.17e-07 ***
## site30R -0.132965 0.875496 0.345753 0.112220 -1.185 0.236075
## site31R -0.330245 0.718747 0.449491 0.071997 -4.587 4.50e-06 ***
## site32R -0.345744 0.707693 0.368594 0.037917 -9.118 < 2e-16 ***
## site33R -0.344040 0.708901 0.332316 0.089733 -3.834 0.000126 ***
## site34R -0.135915 0.872917 0.315011 0.050318 -2.701 0.006911 **
## site35R -0.110211 0.895645 0.333871 0.041637 -2.647 0.008122 **
## site36R -0.161577 0.850801 0.345970 0.053016 -3.048 0.002306 **
## site37R -0.128710 0.879229 0.341341 0.064060 -2.009 0.044514 *
## site38R -0.292125 0.746675 0.328997 0.037171 -7.859 3.88e-15 ***
## site39R -0.185581 0.830622 0.362570 0.034017 -5.456 4.88e-08 ***
## site40R 0.280770 1.324149 0.370757 0.116725 2.405 0.016155 *
## site41R -0.174710 0.839700 0.371677 0.048949 -3.569 0.000358 ***
## site42R 0.121714 1.129431 0.363395 0.033424 3.642 0.000271 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NO3 0.9464 1.0566 0.8873 1.0095
## dx_yr 1.1079 0.9026 0.9554 1.2847
## site02R 2.0476 0.4884 1.7672 2.3726
## site03R 0.7729 1.2939 0.7242 0.8248
## site04R 0.8612 1.1611 0.7137 1.0392
## site05R 0.7538 1.3266 0.6904 0.8230
## site06R 0.9703 1.0306 0.8408 1.1198
## site07R 0.7668 1.3041 0.6202 0.9481
## site09R 0.9881 1.0121 0.9298 1.0500
## site1 1.3158 0.7600 1.0667 1.6230
## site101 0.9252 1.0808 0.7008 1.2216
## site102 0.7976 1.2537 0.6376 0.9979
## site103 1.0990 0.9099 0.9232 1.3084
## site104 0.8269 1.2094 0.6912 0.9892
## site105 1.1101 0.9008 0.8282 1.4879
## site106 0.8457 1.1824 0.6951 1.0290
## site107 1.2645 0.7908 0.9241 1.7302
## site108 1.1846 0.8441 0.7891 1.7785
## site10R 0.8569 1.1669 0.7446 0.9862
## site11R 0.9434 1.0600 0.8321 1.0695
## site12R 0.9453 1.0579 0.8680 1.0295
## site13R 0.7632 1.3103 0.6268 0.9293
## site14R 0.7281 1.3734 0.5718 0.9272
## site15R 1.0325 0.9685 1.0035 1.0623
## site16R 1.1904 0.8401 1.1027 1.2850
## site17R 1.0964 0.9121 1.0339 1.1626
## site18R 0.7730 1.2936 0.5660 1.0558
## site19R 0.9583 1.0435 0.9402 0.9769
## site20R 0.7925 1.2619 0.7290 0.8615
## site21R 0.7378 1.3554 0.6666 0.8165
## site22R 0.9771 1.0234 0.9073 1.0523
## site23R 0.8633 1.1584 0.8225 0.9061
## site24R 0.8952 1.1170 0.7858 1.0199
## site25R 1.0265 0.9741 0.9150 1.1517
## site26R 0.6633 1.5077 0.5635 0.7807
## site27R 1.0284 0.9724 0.8162 1.2957
## site28R 0.9150 1.0929 0.8296 1.0093
## site29R 0.7534 1.3274 0.6784 0.8366
## site30R 0.8755 1.1422 0.7026 1.0909
## site31R 0.7187 1.3913 0.6242 0.8277
## site32R 0.7077 1.4130 0.6570 0.7623
## site33R 0.7089 1.4106 0.5946 0.8452
## site34R 0.8729 1.1456 0.7909 0.9634
## site35R 0.8956 1.1165 0.8255 0.9718
## site36R 0.8508 1.1754 0.7668 0.9440
## site37R 0.8792 1.1374 0.7755 0.9968
## site38R 0.7467 1.3393 0.6942 0.8031
## site39R 0.8306 1.2039 0.7770 0.8879
## site40R 1.3241 0.7552 1.0534 1.6645
## site41R 0.8397 1.1909 0.7629 0.9243
## site42R 1.1294 0.8854 1.0578 1.2059
##
## Concordance= 0.595 (se = 0.044 )
## Likelihood ratio test= 348.4 on 51 df, p=<2e-16
## Wald test = 3.92 on 51 df, p=1
## Score (logrank) test = 337.7 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ NO3 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NO3 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All_IPF,
## id = ID, cluster = cohort)
##
## n= 121241, number of events= 2709
## (2849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## NO3 -0.054564 0.946898 0.072494 0.030757 -1.774 0.076057 .
## dx_yr 0.099218 1.104307 0.007606 0.081135 1.223 0.221376
## age_dx 0.004243 1.004252 0.002446 0.002134 1.988 0.046801 *
## sexF -0.159836 0.852283 0.044922 0.085770 -1.864 0.062386 .
## dich_RaceNon-White 0.018578 1.018752 0.068696 0.061005 0.305 0.760723
## smokeHxFormer 0.098557 1.103577 0.059375 0.039863 2.472 0.013422 *
## smokeHxAlways -0.030814 0.969656 0.138343 0.115025 -0.268 0.788788
## smokeHxUnknown 0.321092 1.378633 0.153737 0.235711 1.362 0.173125
## smokeHxEver 0.036511 1.037186 0.064244 0.008971 4.070 4.70e-05 ***
## disadv 0.042646 1.043569 0.070464 0.126598 0.337 0.736221
## site02R 0.705684 2.025231 0.350996 0.102743 6.868 6.49e-12 ***
## site03R -0.234234 0.791177 0.357870 0.040562 -5.775 7.71e-09 ***
## site04R -0.142465 0.867218 0.468683 0.122649 -1.162 0.245414
## site05R -0.277113 0.757968 0.383738 0.032977 -8.403 < 2e-16 ***
## site06R -0.040991 0.959838 0.338136 0.091358 -0.449 0.653661
## site07R -0.272326 0.761606 0.325345 0.118280 -2.302 0.021314 *
## site09R -0.058391 0.943281 0.353326 0.026494 -2.204 0.027527 *
## site1 0.208889 1.232308 0.300071 0.135454 1.542 0.123041
## site101 -0.127240 0.880522 0.311236 0.136469 -0.932 0.351142
## site102 -0.294134 0.745177 0.313901 0.113372 -2.594 0.009475 **
## site103 0.019954 1.020154 0.301973 0.055022 0.363 0.716867
## site104 -0.253621 0.775985 0.310452 0.063839 -3.973 7.10e-05 ***
## site105 0.047810 1.048971 0.307847 0.139797 0.342 0.732355
## site106 -0.244867 0.782809 0.307248 0.090617 -2.702 0.006888 **
## site107 0.143008 1.153739 0.343367 0.143180 0.999 0.317892
## site108 0.096605 1.101425 0.383039 0.193061 0.500 0.616804
## site10R -0.218385 0.803816 0.396007 0.051323 -4.255 2.09e-05 ***
## site11R -0.090080 0.913858 0.313636 0.057871 -1.557 0.119574
## site12R -0.075753 0.927046 0.350124 0.034140 -2.219 0.026496 *
## site13R -0.306842 0.735767 0.315441 0.110162 -2.785 0.005346 **
## site14R -0.321815 0.724832 0.649699 0.074691 -4.309 1.64e-05 ***
## site15R -0.006817 0.993207 0.342528 0.006763 -1.008 0.313501
## site16R 0.178820 1.195806 0.357268 0.025752 6.944 3.81e-12 ***
## site17R 0.079280 1.082508 0.376556 0.022848 3.470 0.000521 ***
## site18R -0.286966 0.750537 0.333607 0.138826 -2.067 0.038726 *
## site19R -0.082713 0.920615 0.360774 0.011581 -7.142 9.19e-13 ***
## site20R -0.227829 0.796260 0.362425 0.033471 -6.807 9.99e-12 ***
## site21R -0.340566 0.711367 0.330833 0.046167 -7.377 1.62e-13 ***
## site22R -0.032772 0.967759 0.323045 0.036951 -0.887 0.375136
## site23R -0.203361 0.815984 0.336895 0.019874 -10.233 < 2e-16 ***
## site24R -0.121742 0.885376 0.349939 0.046920 -2.595 0.009467 **
## site25R 0.011184 1.011247 0.342676 0.082883 0.135 0.892663
## site26R -0.448746 0.638428 0.355354 0.062591 -7.170 7.53e-13 ***
## site27R 0.045162 1.046197 0.769068 0.135030 0.334 0.738032
## site28R -0.087361 0.916346 0.447924 0.039098 -2.234 0.025455 *
## site29R -0.286051 0.751224 0.443920 0.089187 -3.207 0.001340 **
## site30R -0.194855 0.822954 0.358505 0.125477 -1.553 0.120443
## site31R -0.364271 0.694703 0.457648 0.068311 -5.333 9.69e-08 ***
## site32R -0.383142 0.681716 0.388935 0.038088 -10.059 < 2e-16 ***
## site33R -0.383915 0.681189 0.345400 0.069945 -5.489 4.05e-08 ***
## site34R -0.149150 0.861440 0.327762 0.056229 -2.653 0.007989 **
## site35R -0.159639 0.852452 0.346919 0.034180 -4.670 3.00e-06 ***
## site36R -0.165895 0.847135 0.357402 0.046568 -3.562 0.000367 ***
## site37R -0.143896 0.865978 0.352327 0.076814 -1.873 0.061028 .
## site38R -0.297396 0.742750 0.340216 0.022537 -13.196 < 2e-16 ***
## site39R -0.225284 0.798290 0.373279 0.033474 -6.730 1.69e-11 ***
## site40R 0.238361 1.269167 0.381394 0.118740 2.007 0.044706 *
## site41R -0.212814 0.808306 0.381070 0.051994 -4.093 4.26e-05 ***
## site42R 0.098230 1.103217 0.373549 0.041853 2.347 0.018924 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NO3 0.9469 1.0561 0.8915 1.0057
## dx_yr 1.1043 0.9055 0.9419 1.2946
## age_dx 1.0043 0.9958 1.0001 1.0085
## sexF 0.8523 1.1733 0.7204 1.0083
## dich_RaceNon-White 1.0188 0.9816 0.9039 1.1481
## smokeHxFormer 1.1036 0.9061 1.0206 1.1933
## smokeHxAlways 0.9697 1.0313 0.7739 1.2149
## smokeHxUnknown 1.3786 0.7254 0.8686 2.1882
## smokeHxEver 1.0372 0.9641 1.0191 1.0556
## disadv 1.0436 0.9583 0.8143 1.3375
## site02R 2.0252 0.4938 1.6558 2.4770
## site03R 0.7912 1.2639 0.7307 0.8566
## site04R 0.8672 1.1531 0.6819 1.1029
## site05R 0.7580 1.3193 0.7105 0.8086
## site06R 0.9598 1.0418 0.8025 1.1481
## site07R 0.7616 1.3130 0.6040 0.9603
## site09R 0.9433 1.0601 0.8955 0.9936
## site1 1.2323 0.8115 0.9450 1.6070
## site101 0.8805 1.1357 0.6739 1.1505
## site102 0.7452 1.3420 0.5967 0.9306
## site103 1.0202 0.9802 0.9159 1.1363
## site104 0.7760 1.2887 0.6847 0.8794
## site105 1.0490 0.9533 0.7976 1.3796
## site106 0.7828 1.2775 0.6554 0.9350
## site107 1.1537 0.8667 0.8714 1.5275
## site108 1.1014 0.9079 0.7544 1.6080
## site10R 0.8038 1.2441 0.7269 0.8889
## site11R 0.9139 1.0943 0.8159 1.0236
## site12R 0.9270 1.0787 0.8670 0.9912
## site13R 0.7358 1.3591 0.5929 0.9131
## site14R 0.7248 1.3796 0.6261 0.8391
## site15R 0.9932 1.0068 0.9801 1.0065
## site16R 1.1958 0.8363 1.1369 1.2577
## site17R 1.0825 0.9238 1.0351 1.1321
## site18R 0.7505 1.3324 0.5717 0.9852
## site19R 0.9206 1.0862 0.9000 0.9418
## site20R 0.7963 1.2559 0.7457 0.8502
## site21R 0.7114 1.4057 0.6498 0.7787
## site22R 0.9678 1.0333 0.9001 1.0404
## site23R 0.8160 1.2255 0.7848 0.8484
## site24R 0.8854 1.1295 0.8076 0.9707
## site25R 1.0112 0.9889 0.8596 1.1896
## site26R 0.6384 1.5663 0.5647 0.7218
## site27R 1.0462 0.9558 0.8029 1.3632
## site28R 0.9163 1.0913 0.8487 0.9893
## site29R 0.7512 1.3312 0.6307 0.8947
## site30R 0.8230 1.2151 0.6435 1.0524
## site31R 0.6947 1.4395 0.6076 0.7942
## site32R 0.6817 1.4669 0.6327 0.7346
## site33R 0.6812 1.4680 0.5939 0.7813
## site34R 0.8614 1.1608 0.7715 0.9618
## site35R 0.8525 1.1731 0.7972 0.9115
## site36R 0.8471 1.1804 0.7732 0.9281
## site37R 0.8660 1.1548 0.7449 1.0067
## site38R 0.7427 1.3463 0.7107 0.7763
## site39R 0.7983 1.2527 0.7476 0.8524
## site40R 1.2692 0.7879 1.0057 1.6017
## site41R 0.8083 1.2372 0.7300 0.8950
## site42R 1.1032 0.9064 1.0163 1.1975
##
## Concordance= 0.598 (se = 0.039 )
## Likelihood ratio test= 380.4 on 59 df, p=<2e-16
## Wald test = 5.61 on 59 df, p=1
## Score (logrank) test = 374.6 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ NH4 + dx_yr, data=Simm_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NH4 + dx_yr, data = Simm_IPF,
## id = ID)
##
## n= 31982, number of events= 695
## (446 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## NH4 0.15537 1.16809 0.13567 1.145 0.2521
## dx_yr 0.03761 1.03832 0.01508 2.494 0.0126 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NH4 1.168 0.8561 0.8953 1.524
## dx_yr 1.038 0.9631 1.0081 1.069
##
## Concordance= 0.522 (se = 0.013 )
## Likelihood ratio test= 8.91 on 2 df, p=0.01
## Wald test = 8.76 on 2 df, p=0.01
## Score (logrank) test = 8.78 on 2 df, p=0.01
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ NH4 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv, data=Simm_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NH4 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv, data = Simm_IPF,
## id = ID)
##
## n= 30488, number of events= 670
## (1940 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## NH4 0.157834 1.170972 0.142271 1.109 0.26726
## dx_yr 0.025811 1.026147 0.016055 1.608 0.10791
## age_dx 0.001899 1.001900 0.004559 0.416 0.67709
## sexF -0.377828 0.685349 0.088779 -4.256 2.08e-05 ***
## dich_RaceNon-White 0.164438 1.178730 0.128565 1.279 0.20089
## smokeHxFormer 0.082867 1.086398 0.091966 0.901 0.36755
## smokeHxAlways -0.403611 0.667904 0.266618 -1.514 0.13007
## smokeHxUnknown 0.496943 1.643689 0.169764 2.927 0.00342 **
## disadv 0.382206 1.465514 0.134148 2.849 0.00438 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NH4 1.1710 0.8540 0.8860 1.5476
## dx_yr 1.0261 0.9745 0.9944 1.0589
## age_dx 1.0019 0.9981 0.9930 1.0109
## sexF 0.6853 1.4591 0.5759 0.8156
## dich_RaceNon-White 1.1787 0.8484 0.9162 1.5165
## smokeHxFormer 1.0864 0.9205 0.9072 1.3010
## smokeHxAlways 0.6679 1.4972 0.3961 1.1263
## smokeHxUnknown 1.6437 0.6084 1.1785 2.2926
## disadv 1.4655 0.6824 1.1267 1.9062
##
## Concordance= 0.591 (se = 0.012 )
## Likelihood ratio test= 52.86 on 9 df, p=3e-08
## Wald test = 53.21 on 9 df, p=3e-08
## Score (logrank) test = 53.95 on 9 df, p=2e-08
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ NH4 + dx_yr + site, data=PFF_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NH4 + dx_yr +
## site, data = PFF_IPF, id = ID)
##
## n= 54499, number of events= 1146
## (217 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## NH4 0.299578 1.349289 0.205870 1.455 0.1456
## dx_yr 0.128522 1.137147 0.021647 5.937 2.9e-09 ***
## site02R 0.563769 1.757282 0.341884 1.649 0.0991 .
## site03R -0.267417 0.765354 0.347439 -0.770 0.4415
## site04R -0.389703 0.677258 0.466576 -0.835 0.4036
## site05R -0.506124 0.602827 0.395179 -1.281 0.2003
## site06R -0.133983 0.874605 0.334200 -0.401 0.6885
## site07R -0.475317 0.621688 0.327158 -1.453 0.1463
## site08R NA NA 0.000000 NA NA
## site09R -0.116651 0.889895 0.347165 -0.336 0.7369
## site10R -0.141840 0.867760 0.385315 -0.368 0.7128
## site11R -0.126524 0.881153 0.303209 -0.417 0.6765
## site12R -0.179617 0.835590 0.340895 -0.527 0.5983
## site13R -0.420066 0.657004 0.315697 -1.331 0.1833
## site14R -0.525120 0.591484 0.652916 -0.804 0.4212
## site15R -0.001739 0.998262 0.331854 -0.005 0.9958
## site16R 0.024294 1.024591 0.353837 0.069 0.9453
## site17R -0.120791 0.886219 0.377038 -0.320 0.7487
## site18R -0.340826 0.711182 0.326550 -1.044 0.2966
## site19R 0.009459 1.009504 0.350178 0.027 0.9785
## site20R -0.358113 0.698994 0.355973 -1.006 0.3144
## site21R -0.358986 0.698384 0.324012 -1.108 0.2679
## site22R -0.220351 0.802237 0.326009 -0.676 0.4991
## site23R -0.244747 0.782902 0.328677 -0.745 0.4565
## site24R -0.251247 0.777830 0.340470 -0.738 0.4605
## site25R -0.091436 0.912620 0.330747 -0.276 0.7822
## site26R -0.484024 0.616299 0.350134 -1.382 0.1669
## site27R -0.152646 0.858434 0.769209 -0.198 0.8427
## site28R -0.322003 0.724696 0.457481 -0.704 0.4815
## site29R -0.374218 0.687827 0.439059 -0.852 0.3940
## site30R -0.395214 0.673536 0.357303 -1.106 0.2687
## site31R -0.397047 0.672302 0.453386 -0.876 0.3812
## site32R -0.354642 0.701425 0.368645 -0.962 0.3360
## site33R -0.428317 0.651605 0.339143 -1.263 0.2066
## site34R -0.255439 0.774577 0.323685 -0.789 0.4300
## site35R -0.160720 0.851530 0.336006 -0.478 0.6324
## site36R -0.410755 0.663149 0.370201 -1.110 0.2672
## site37R -0.367693 0.692329 0.363746 -1.011 0.3121
## site38R -0.352701 0.702787 0.332979 -1.059 0.2895
## site39R -0.252164 0.777118 0.361934 -0.697 0.4860
## site40R -0.061528 0.940327 0.379882 -0.162 0.8713
## site41R -0.320767 0.725592 0.378005 -0.849 0.3961
## site42R -0.073911 0.928754 0.376203 -0.196 0.8442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NH4 1.3493 0.7411 0.9013 2.020
## dx_yr 1.1371 0.8794 1.0899 1.186
## site02R 1.7573 0.5691 0.8991 3.434
## site03R 0.7654 1.3066 0.3874 1.512
## site04R 0.6773 1.4765 0.2714 1.690
## site05R 0.6028 1.6588 0.2779 1.308
## site06R 0.8746 1.1434 0.4543 1.684
## site07R 0.6217 1.6085 0.3274 1.180
## site08R NA NA NA NA
## site09R 0.8899 1.1237 0.4506 1.757
## site10R 0.8678 1.1524 0.4078 1.847
## site11R 0.8812 1.1349 0.4864 1.596
## site12R 0.8356 1.1968 0.4284 1.630
## site13R 0.6570 1.5221 0.3539 1.220
## site14R 0.5915 1.6907 0.1645 2.127
## site15R 0.9983 1.0017 0.5209 1.913
## site16R 1.0246 0.9760 0.5121 2.050
## site17R 0.8862 1.1284 0.4233 1.856
## site18R 0.7112 1.4061 0.3750 1.349
## site19R 1.0095 0.9906 0.5082 2.005
## site20R 0.6990 1.4306 0.3479 1.404
## site21R 0.6984 1.4319 0.3701 1.318
## site22R 0.8022 1.2465 0.4235 1.520
## site23R 0.7829 1.2773 0.4111 1.491
## site24R 0.7778 1.2856 0.3991 1.516
## site25R 0.9126 1.0957 0.4773 1.745
## site26R 0.6163 1.6226 0.3103 1.224
## site27R 0.8584 1.1649 0.1901 3.877
## site28R 0.7247 1.3799 0.2956 1.776
## site29R 0.6878 1.4539 0.2909 1.626
## site30R 0.6735 1.4847 0.3344 1.357
## site31R 0.6723 1.4874 0.2765 1.635
## site32R 0.7014 1.4257 0.3406 1.445
## site33R 0.6516 1.5347 0.3352 1.267
## site34R 0.7746 1.2910 0.4107 1.461
## site35R 0.8515 1.1744 0.4407 1.645
## site36R 0.6631 1.5080 0.3210 1.370
## site37R 0.6923 1.4444 0.3394 1.412
## site38R 0.7028 1.4229 0.3659 1.350
## site39R 0.7771 1.2868 0.3823 1.580
## site40R 0.9403 1.0635 0.4466 1.980
## site41R 0.7256 1.3782 0.3459 1.522
## site42R 0.9288 1.0767 0.4443 1.941
##
## Concordance= 0.59 (se = 0.009 )
## Likelihood ratio test= 112.6 on 42 df, p=2e-08
## Wald test = 111.5 on 42 df, p=3e-08
## Score (logrank) test = 115.5 on 42 df, p=9e-09
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ NH4 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=PFF_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NH4 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = PFF_IPF,
## id = ID)
##
## n= 53867, number of events= 1131
## (849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## NH4 0.3099396 1.3633427 0.2089333 1.483 0.138
## dx_yr 0.1282427 1.1368289 0.0222315 5.769 8e-09 ***
## age_dx -0.0002757 0.9997243 0.0039383 -0.070 0.944
## sexM 0.0603721 1.0622317 0.0713335 0.846 0.397
## dich_RaceNon-White -0.0675310 0.9346988 0.1263415 -0.535 0.593
## smokeHxEver 0.0425310 1.0434484 0.0646444 0.658 0.511
## disadv -0.0451066 0.9558956 0.1121488 -0.402 0.688
## site02R 0.5110305 1.6670081 0.3544584 1.442 0.149
## site03R -0.2976238 0.7425807 0.3597288 -0.827 0.408
## site04R -0.4486624 0.6384816 0.4774194 -0.940 0.347
## site05R -0.5561066 0.5734374 0.4078989 -1.363 0.173
## site06R -0.1744525 0.8399168 0.3501181 -0.498 0.618
## site07R -0.5229806 0.5927512 0.3414992 -1.531 0.126
## site08R NA NA 0.0000000 NA NA
## site09R -0.1988847 0.8196444 0.3617123 -0.550 0.582
## site10R -0.1998395 0.8188622 0.3985339 -0.501 0.616
## site11R -0.1880904 0.8285398 0.3213644 -0.585 0.558
## site12R -0.2278811 0.7962189 0.3551676 -0.642 0.521
## site13R -0.4801375 0.6186983 0.3290952 -1.459 0.145
## site14R -0.5908412 0.5538612 0.6606571 -0.894 0.371
## site15R -0.0526096 0.9487504 0.3474469 -0.151 0.880
## site16R -0.0217557 0.9784793 0.3651909 -0.060 0.952
## site17R -0.1826296 0.8330767 0.3907721 -0.467 0.640
## site18R -0.3788716 0.6846335 0.3421525 -1.107 0.268
## site19R -0.0369094 0.9637634 0.3625156 -0.102 0.919
## site20R -0.4132719 0.6614824 0.3700763 -1.117 0.264
## site21R -0.4042978 0.6674453 0.3376549 -1.197 0.231
## site22R -0.2773252 0.7578080 0.3402512 -0.815 0.415
## site23R -0.3119271 0.7320349 0.3445775 -0.905 0.365
## site24R -0.3101700 0.7333223 0.3544824 -0.875 0.382
## site25R -0.1289293 0.8790361 0.3442595 -0.375 0.708
## site26R -0.5212456 0.5937805 0.3634313 -1.434 0.152
## site27R -0.2609816 0.7702951 0.7804243 -0.334 0.738
## site28R -0.3607865 0.6971278 0.4697816 -0.768 0.442
## site29R -0.4300704 0.6504633 0.4505923 -0.954 0.340
## site30R -0.4558110 0.6339337 0.3706741 -1.230 0.219
## site31R -0.4493241 0.6380593 0.4625708 -0.971 0.331
## site32R -0.4218615 0.6558249 0.3905394 -1.080 0.280
## site33R -0.4697900 0.6251335 0.3545632 -1.325 0.185
## site34R -0.3022780 0.7391326 0.3393083 -0.891 0.373
## site35R -0.2220664 0.8008622 0.3504066 -0.634 0.526
## site36R -0.4512105 0.6368568 0.3854093 -1.171 0.242
## site37R -0.4167212 0.6592047 0.3768232 -1.106 0.269
## site38R -0.3925347 0.6753429 0.3466539 -1.132 0.257
## site39R -0.3055217 0.7367389 0.3745333 -0.816 0.415
## site40R -0.0973459 0.9072421 0.3906020 -0.249 0.803
## site41R -0.3619630 0.6963081 0.3885240 -0.932 0.352
## site42R -0.1427495 0.8669712 0.3884525 -0.367 0.713
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NH4 1.3633 0.7335 0.9052 2.053
## dx_yr 1.1368 0.8796 1.0884 1.187
## age_dx 0.9997 1.0003 0.9920 1.007
## sexM 1.0622 0.9414 0.9236 1.222
## dich_RaceNon-White 0.9347 1.0699 0.7297 1.197
## smokeHxEver 1.0434 0.9584 0.9193 1.184
## disadv 0.9559 1.0461 0.7673 1.191
## site02R 1.6670 0.5999 0.8322 3.339
## site03R 0.7426 1.3467 0.3669 1.503
## site04R 0.6385 1.5662 0.2505 1.628
## site05R 0.5734 1.7439 0.2578 1.276
## site06R 0.8399 1.1906 0.4229 1.668
## site07R 0.5928 1.6870 0.3035 1.158
## site08R NA NA NA NA
## site09R 0.8196 1.2200 0.4034 1.665
## site10R 0.8189 1.2212 0.3750 1.788
## site11R 0.8285 1.2069 0.4413 1.555
## site12R 0.7962 1.2559 0.3969 1.597
## site13R 0.6187 1.6163 0.3246 1.179
## site14R 0.5539 1.8055 0.1517 2.022
## site15R 0.9488 1.0540 0.4802 1.875
## site16R 0.9785 1.0220 0.4783 2.002
## site17R 0.8331 1.2004 0.3873 1.792
## site18R 0.6846 1.4606 0.3501 1.339
## site19R 0.9638 1.0376 0.4736 1.961
## site20R 0.6615 1.5118 0.3203 1.366
## site21R 0.6674 1.4983 0.3444 1.294
## site22R 0.7578 1.3196 0.3890 1.476
## site23R 0.7320 1.3661 0.3726 1.438
## site24R 0.7333 1.3637 0.3661 1.469
## site25R 0.8790 1.1376 0.4477 1.726
## site26R 0.5938 1.6841 0.2913 1.211
## site27R 0.7703 1.2982 0.1669 3.556
## site28R 0.6971 1.4345 0.2776 1.751
## site29R 0.6505 1.5374 0.2690 1.573
## site30R 0.6339 1.5775 0.3066 1.311
## site31R 0.6381 1.5673 0.2577 1.580
## site32R 0.6558 1.5248 0.3050 1.410
## site33R 0.6251 1.5997 0.3120 1.252
## site34R 0.7391 1.3529 0.3801 1.437
## site35R 0.8009 1.2487 0.4030 1.592
## site36R 0.6369 1.5702 0.2992 1.356
## site37R 0.6592 1.5170 0.3150 1.380
## site38R 0.6753 1.4807 0.3423 1.332
## site39R 0.7367 1.3573 0.3536 1.535
## site40R 0.9072 1.1022 0.4219 1.951
## site41R 0.6963 1.4361 0.3252 1.491
## site42R 0.8670 1.1534 0.4049 1.856
##
## Concordance= 0.591 (se = 0.009 )
## Likelihood ratio test= 113.4 on 47 df, p=2e-07
## Wald test = 112.6 on 47 df, p=3e-07
## Score (logrank) test = 116.6 on 47 df, p=8e-08
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ NH4 + dx_yr + site, data=CARE_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NH4 + dx_yr +
## site, data = CARE_IPF, id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## NH4 -2.32104 0.09817 0.43145 -5.380 7.46e-08 ***
## dx_yr 0.72763 2.07017 0.03556 20.463 < 2e-16 ***
## site102 -0.35162 0.70355 0.17863 -1.968 0.04902 *
## site103 -0.19000 0.82696 0.17429 -1.090 0.27567
## site104 -0.33236 0.71723 0.18811 -1.767 0.07726 .
## site105 -0.12298 0.88429 0.12980 -0.947 0.34341
## site106 0.35328 1.42373 0.12770 2.767 0.00566 **
## site107 -0.04042 0.96038 0.19850 -0.204 0.83863
## site108 -0.81867 0.44102 0.27338 -2.995 0.00275 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NH4 0.09817 10.1862 0.04214 0.2287
## dx_yr 2.07017 0.4831 1.93080 2.2196
## site102 0.70355 1.4214 0.49573 0.9985
## site103 0.82696 1.2092 0.58766 1.1637
## site104 0.71723 1.3943 0.49606 1.0370
## site105 0.88429 1.1309 0.68566 1.1405
## site106 1.42373 0.7024 1.10849 1.8286
## site107 0.96038 1.0413 0.65085 1.4171
## site108 0.44102 2.2675 0.25808 0.7536
##
## Concordance= 0.777 (se = 0.01 )
## Likelihood ratio test= 1080 on 9 df, p=<2e-16
## Wald test = 691.6 on 9 df, p=<2e-16
## Score (logrank) test = 586.3 on 9 df, p=<2e-16
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ NH4 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=CARE_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NH4 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = CARE_IPF,
## id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## NH4 -2.417776 0.089120 0.437706 -5.524 3.32e-08 ***
## dx_yr 0.723758 2.062168 0.035768 20.235 < 2e-16 ***
## age_dx 0.013063 1.013149 0.004556 2.867 0.00414 **
## sexF -0.048076 0.953061 0.077150 -0.623 0.53318
## dich_RaceNon-White -0.142324 0.867340 0.110735 -1.285 0.19870
## smokeHxFormer -0.016654 0.983483 0.081456 -0.204 0.83799
## smokeHxAlways 0.054267 1.055767 0.168460 0.322 0.74735
## smokeHxUnknown 0.435943 1.546421 0.734682 0.593 0.55293
## disadv 0.022015 1.022259 0.130595 0.169 0.86613
## site102 -0.393999 0.674355 0.181678 -2.169 0.03011 *
## site103 -0.209966 0.810611 0.179209 -1.172 0.24134
## site104 -0.356652 0.700016 0.190645 -1.871 0.06138 .
## site105 -0.114527 0.891788 0.133049 -0.861 0.38936
## site106 0.325792 1.385128 0.128596 2.533 0.01129 *
## site107 -0.118235 0.888487 0.206228 -0.573 0.56643
## site108 -0.839694 0.431843 0.278327 -3.017 0.00255 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NH4 0.08912 11.2209 0.03779 0.2102
## dx_yr 2.06217 0.4849 1.92255 2.2119
## age_dx 1.01315 0.9870 1.00414 1.0222
## sexF 0.95306 1.0493 0.81932 1.1086
## dich_RaceNon-White 0.86734 1.1530 0.69812 1.0776
## smokeHxFormer 0.98348 1.0168 0.83836 1.1537
## smokeHxAlways 1.05577 0.9472 0.75888 1.4688
## smokeHxUnknown 1.54642 0.6467 0.36641 6.5266
## disadv 1.02226 0.9782 0.79140 1.3205
## site102 0.67435 1.4829 0.47233 0.9628
## site103 0.81061 1.2336 0.57052 1.1517
## site104 0.70002 1.4285 0.48176 1.0172
## site105 0.89179 1.1213 0.68708 1.1575
## site106 1.38513 0.7220 1.07654 1.7822
## site107 0.88849 1.1255 0.59307 1.3310
## site108 0.43184 2.3157 0.25027 0.7451
##
## Concordance= 0.778 (se = 0.01 )
## Likelihood ratio test= 1092 on 16 df, p=<2e-16
## Wald test = 698.9 on 16 df, p=<2e-16
## Score (logrank) test = 593.3 on 16 df, p=<2e-16
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ NH4 + dx_yr + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NH4 + dx_yr +
## site, data = All_IPF, id = ID, cluster = cohort)
##
## n= 123367, number of events= 2749
## (723 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## NH4 0.93181 2.53910 0.10745 0.59258 1.572 0.115847
## dx_yr 0.18588 1.20428 0.01202 0.12022 1.546 0.122068
## site02R 0.36071 1.43435 0.33619 0.23780 1.517 0.129299
## site03R -0.34668 0.70703 0.34717 0.07548 -4.593 4.37e-06 ***
## site04R -0.79110 0.45335 0.45390 0.39835 -1.986 0.047039 *
## site05R -0.96392 0.38139 0.37555 0.36876 -2.614 0.008949 **
## site06R -0.38543 0.68016 0.32729 0.13282 -2.902 0.003708 **
## site07R -0.86156 0.42250 0.31083 0.40396 -2.133 0.032941 *
## site09R -0.35739 0.69950 0.34172 0.18457 -1.936 0.052827 .
## site1 -0.42537 0.65353 0.29210 0.30974 -1.373 0.169655
## site101 -0.51725 0.59616 0.29687 0.35670 -1.450 0.147026
## site102 -0.33333 0.71653 0.29810 0.14373 -2.319 0.020391 *
## site103 0.07895 1.08215 0.28631 0.07879 1.002 0.316330
## site104 -0.22962 0.79483 0.29475 0.10129 -2.267 0.023388 *
## site105 -0.25463 0.77520 0.29203 0.31408 -0.811 0.417528
## site106 -0.70545 0.49389 0.29172 0.37095 -1.902 0.057206 .
## site107 -0.14332 0.86648 0.32915 0.33046 -0.434 0.664510
## site108 -0.12451 0.88293 0.37065 0.32331 -0.385 0.700162
## site10R -0.14188 0.86773 0.38522 0.05019 -2.827 0.004699 **
## site11R -0.27530 0.75935 0.30027 0.16124 -1.707 0.087750 .
## site12R -0.40518 0.66686 0.33564 0.15484 -2.617 0.008876 **
## site13R -0.71628 0.48857 0.30540 0.31729 -2.257 0.023978 *
## site14R -0.91750 0.39951 0.64423 0.21534 -4.261 2.04e-05 ***
## site15R -0.09940 0.90538 0.33074 0.07158 -1.389 0.164939
## site16R -0.22061 0.80203 0.34675 0.16603 -1.329 0.183944
## site17R -0.46256 0.62967 0.36445 0.28866 -1.602 0.109059
## site18R -0.50355 0.60438 0.32270 0.26858 -1.875 0.060807 .
## site19R 0.02740 1.02778 0.34998 0.04060 0.675 0.499785
## site20R -0.62287 0.53640 0.34928 0.20077 -3.102 0.001920 **
## site21R -0.55516 0.57398 0.32048 0.16616 -3.341 0.000835 ***
## site22R -0.58779 0.55555 0.31087 0.29454 -1.996 0.045975 *
## site23R -0.44893 0.63831 0.32397 0.16762 -2.678 0.007401 **
## site24R -0.51735 0.59610 0.33300 0.16627 -3.111 0.001862 **
## site25R -0.31624 0.72888 0.32700 0.24015 -1.317 0.187892
## site26R -0.70005 0.49656 0.34604 0.25294 -2.768 0.005646 **
## site27R -0.56310 0.56944 0.76217 0.41860 -1.345 0.178561
## site28R -0.79281 0.45257 0.44002 0.37497 -2.114 0.034487 *
## site29R -0.61067 0.54299 0.43499 0.20375 -2.997 0.002725 **
## site30R -0.82606 0.43777 0.33778 0.43788 -1.887 0.059227 .
## site31R -0.60412 0.54655 0.45039 0.20443 -2.955 0.003126 **
## site32R -0.39733 0.67211 0.36854 0.06318 -6.289 3.20e-10 ***
## site33R -0.66478 0.51438 0.33403 0.26183 -2.539 0.011117 *
## site34R -0.52304 0.59272 0.31602 0.24352 -2.148 0.031729 *
## site35R -0.28676 0.75069 0.33429 0.11336 -2.530 0.011419 *
## site36R -0.89950 0.40677 0.34633 0.39776 -2.261 0.023733 *
## site37R -0.83734 0.43286 0.34230 0.40559 -2.065 0.038967 *
## site38R -0.52747 0.59010 0.32992 0.14344 -3.677 0.000236 ***
## site39R -0.38544 0.68015 0.36051 0.10887 -3.540 0.000400 ***
## site40R -0.58460 0.55733 0.35355 0.55890 -1.046 0.295572
## site41R -0.59247 0.55296 0.37094 0.23678 -2.502 0.012342 *
## site42R -0.40822 0.66483 0.36414 0.29518 -1.383 0.166681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NH4 2.5391 0.3938 0.7948 8.1113
## dx_yr 1.2043 0.8304 0.9515 1.5243
## site02R 1.4343 0.6972 0.9000 2.2860
## site03R 0.7070 1.4144 0.6098 0.8198
## site04R 0.4533 2.2058 0.2077 0.9897
## site05R 0.3814 2.6220 0.1851 0.7857
## site06R 0.6802 1.4703 0.5243 0.8824
## site07R 0.4225 2.3669 0.1914 0.9326
## site09R 0.6995 1.4296 0.4872 1.0044
## site1 0.6535 1.5302 0.3561 1.1993
## site101 0.5962 1.6774 0.2963 1.1994
## site102 0.7165 1.3956 0.5406 0.9497
## site103 1.0822 0.9241 0.9273 1.2629
## site104 0.7948 1.2581 0.6517 0.9694
## site105 0.7752 1.2900 0.4189 1.4347
## site106 0.4939 2.0248 0.2387 1.0218
## site107 0.8665 1.1541 0.4534 1.6560
## site108 0.8829 1.1326 0.4685 1.6639
## site10R 0.8677 1.1524 0.7864 0.9574
## site11R 0.7593 1.3169 0.5536 1.0416
## site12R 0.6669 1.4996 0.4923 0.9033
## site13R 0.4886 2.0468 0.2623 0.9099
## site14R 0.3995 2.5030 0.2620 0.6093
## site15R 0.9054 1.1045 0.7869 1.0417
## site16R 0.8020 1.2468 0.5792 1.1105
## site17R 0.6297 1.5881 0.3576 1.1087
## site18R 0.6044 1.6546 0.3570 1.0231
## site19R 1.0278 0.9730 0.9492 1.1129
## site20R 0.5364 1.8643 0.3619 0.7950
## site21R 0.5740 1.7422 0.4144 0.7949
## site22R 0.5556 1.8000 0.3119 0.9896
## site23R 0.6383 1.5666 0.4596 0.8866
## site24R 0.5961 1.6776 0.4303 0.8257
## site25R 0.7289 1.3720 0.4552 1.1670
## site26R 0.4966 2.0139 0.3025 0.8152
## site27R 0.5694 1.7561 0.2507 1.2935
## site28R 0.4526 2.2096 0.2170 0.9438
## site29R 0.5430 1.8417 0.3642 0.8095
## site30R 0.4378 2.2843 0.1856 1.0327
## site31R 0.5466 1.8296 0.3661 0.8159
## site32R 0.6721 1.4878 0.5938 0.7607
## site33R 0.5144 1.9441 0.3079 0.8593
## site34R 0.5927 1.6872 0.3678 0.9553
## site35R 0.7507 1.3321 0.6011 0.9375
## site36R 0.4068 2.4584 0.1865 0.8870
## site37R 0.4329 2.3102 0.1955 0.9585
## site38R 0.5901 1.6946 0.4455 0.7817
## site39R 0.6802 1.4703 0.5495 0.8419
## site40R 0.5573 1.7943 0.1864 1.6667
## site41R 0.5530 1.8084 0.3477 0.8795
## site42R 0.6648 1.5041 0.3728 1.1857
##
## Concordance= 0.602 (se = 0.043 )
## Likelihood ratio test= 421.9 on 51 df, p=<2e-16
## Wald test = 2.47 on 51 df, p=1
## Score (logrank) test = 394.6 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ NH4 + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ NH4 + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All_IPF,
## id = ID, cluster = cohort)
##
## n= 121241, number of events= 2709
## (2849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## NH4 0.967106 2.630322 0.109539 0.610140 1.585 0.112954
## dx_yr 0.186356 1.204851 0.012442 0.126113 1.478 0.139491
## age_dx 0.003553 1.003559 0.002438 0.001808 1.965 0.049390 *
## sexF -0.142803 0.866925 0.044902 0.085819 -1.664 0.096113 .
## dich_RaceNon-White 0.015770 1.015895 0.068933 0.071316 0.221 0.824992
## smokeHxFormer 0.094836 1.099479 0.059305 0.022491 4.217 2.48e-05 ***
## smokeHxAlways -0.018509 0.981661 0.138598 0.099732 -0.186 0.852768
## smokeHxUnknown 0.303964 1.355220 0.152997 0.195234 1.557 0.119489
## smokeHxEver 0.034896 1.035512 0.064259 0.004294 8.127 4.41e-16 ***
## disadv 0.052803 1.054222 0.070526 0.133998 0.394 0.693537
## site02R 0.338177 1.402389 0.346886 0.251039 1.347 0.177945
## site03R -0.325112 0.722446 0.357472 0.083079 -3.913 9.10e-05 ***
## site04R -0.798887 0.449829 0.462200 0.417239 -1.915 0.055531 .
## site05R -0.981103 0.374898 0.385961 0.371306 -2.642 0.008234 **
## site06R -0.406819 0.665765 0.340081 0.105357 -3.861 0.000113 ***
## site07R -0.883017 0.413533 0.322387 0.404345 -2.184 0.028975 *
## site09R -0.409306 0.664111 0.353977 0.178265 -2.296 0.021673 *
## site1 -0.514475 0.597814 0.308255 0.286044 -1.799 0.072084 .
## site101 -0.579353 0.560261 0.311843 0.351149 -1.650 0.098968 .
## site102 -0.396634 0.672580 0.313941 0.146336 -2.710 0.006720 **
## site103 0.008494 1.008530 0.301975 0.048669 0.175 0.861456
## site104 -0.291553 0.747102 0.310558 0.078843 -3.698 0.000217 ***
## site105 -0.321905 0.724767 0.309137 0.303786 -1.060 0.289306
## site106 -0.794692 0.451720 0.307490 0.362859 -2.190 0.028518 *
## site107 -0.243152 0.784152 0.344712 0.315544 -0.771 0.440955
## site108 -0.202172 0.816954 0.382962 0.310351 -0.651 0.514769
## site10R -0.198144 0.820252 0.395988 0.027688 -7.156 8.29e-13 ***
## site11R -0.318822 0.727005 0.314582 0.151530 -2.104 0.035377 *
## site12R -0.432710 0.648749 0.346808 0.146472 -2.954 0.003135 **
## site13R -0.761453 0.466987 0.316862 0.320768 -2.374 0.017604 *
## site14R -0.942307 0.389728 0.650155 0.230033 -4.096 4.20e-05 ***
## site15R -0.142418 0.867259 0.342924 0.049604 -2.871 0.004091 **
## site16R -0.229185 0.795181 0.356837 0.154980 -1.479 0.139191
## site17R -0.489918 0.612677 0.375235 0.287169 -1.706 0.088003 .
## site18R -0.540658 0.582365 0.334656 0.243692 -2.219 0.026513 *
## site19R -0.008249 0.991785 0.360654 0.052008 -0.159 0.873970
## site20R -0.630472 0.532340 0.360593 0.202499 -3.113 0.001849 **
## site21R -0.595138 0.551486 0.331776 0.153498 -3.877 0.000106 ***
## site22R -0.610871 0.542878 0.322627 0.301676 -2.025 0.042875 *
## site23R -0.512360 0.599080 0.337655 0.148170 -3.458 0.000544 ***
## site24R -0.535427 0.585419 0.344579 0.156175 -3.428 0.000607 ***
## site25R -0.350178 0.704563 0.338823 0.263175 -1.331 0.183324
## site26R -0.746592 0.473979 0.356864 0.231256 -3.228 0.001245 **
## site27R -0.562870 0.569572 0.769697 0.426023 -1.321 0.186428
## site28R -0.812840 0.443596 0.448808 0.368002 -2.209 0.027189 *
## site29R -0.619752 0.538078 0.444140 0.236344 -2.622 0.008735 **
## site30R -0.908980 0.402935 0.349699 0.438725 -2.072 0.038278 *
## site31R -0.642008 0.526235 0.458496 0.197200 -3.256 0.001131 **
## site32R -0.458704 0.632103 0.388757 0.068145 -6.731 1.68e-11 ***
## site33R -0.698631 0.497266 0.346948 0.230433 -3.032 0.002431 **
## site34R -0.544555 0.580100 0.328612 0.246292 -2.211 0.027035 *
## site35R -0.336813 0.714042 0.347214 0.101148 -3.330 0.000869 ***
## site36R -0.926335 0.396002 0.357491 0.394576 -2.348 0.018891 *
## site37R -0.868827 0.419443 0.352959 0.414290 -2.097 0.035981 *
## site38R -0.539272 0.583172 0.341158 0.121181 -4.450 8.58e-06 ***
## site39R -0.428334 0.651594 0.371070 0.097419 -4.397 1.10e-05 ***
## site40R -0.654111 0.519904 0.363493 0.549066 -1.191 0.233530
## site41R -0.638739 0.527958 0.380139 0.227405 -2.809 0.004972 **
## site42R -0.442818 0.642224 0.374191 0.299865 -1.477 0.139749
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## NH4 2.6303 0.3802 0.7955 8.6968
## dx_yr 1.2049 0.8300 0.9410 1.5427
## age_dx 1.0036 0.9965 1.0000 1.0071
## sexF 0.8669 1.1535 0.7327 1.0257
## dich_RaceNon-White 1.0159 0.9844 0.8834 1.1683
## smokeHxFormer 1.0995 0.9095 1.0521 1.1490
## smokeHxAlways 0.9817 1.0187 0.8074 1.1936
## smokeHxUnknown 1.3552 0.7379 0.9243 1.9870
## smokeHxEver 1.0355 0.9657 1.0268 1.0443
## disadv 1.0542 0.9486 0.8107 1.3709
## site02R 1.4024 0.7131 0.8574 2.2938
## site03R 0.7224 1.3842 0.6139 0.8502
## site04R 0.4498 2.2231 0.1986 1.0191
## site05R 0.3749 2.6674 0.1811 0.7762
## site06R 0.6658 1.5020 0.5416 0.8185
## site07R 0.4135 2.4182 0.1872 0.9135
## site09R 0.6641 1.5058 0.4683 0.9418
## site1 0.5978 1.6728 0.3413 1.0472
## site101 0.5603 1.7849 0.2815 1.1150
## site102 0.6726 1.4868 0.5049 0.8960
## site103 1.0085 0.9915 0.9168 1.1095
## site104 0.7471 1.3385 0.6401 0.8719
## site105 0.7248 1.3798 0.3996 1.3146
## site106 0.4517 2.2138 0.2218 0.9199
## site107 0.7842 1.2753 0.4225 1.4554
## site108 0.8170 1.2241 0.4447 1.5010
## site10R 0.8203 1.2191 0.7769 0.8660
## site11R 0.7270 1.3755 0.5402 0.9784
## site12R 0.6487 1.5414 0.4869 0.8645
## site13R 0.4670 2.1414 0.2490 0.8757
## site14R 0.3897 2.5659 0.2483 0.6117
## site15R 0.8673 1.1531 0.7869 0.9558
## site16R 0.7952 1.2576 0.5869 1.0774
## site17R 0.6127 1.6322 0.3490 1.0756
## site18R 0.5824 1.7171 0.3612 0.9389
## site19R 0.9918 1.0083 0.8957 1.0982
## site20R 0.5323 1.8785 0.3579 0.7917
## site21R 0.5515 1.8133 0.4082 0.7451
## site22R 0.5429 1.8420 0.3005 0.9806
## site23R 0.5991 1.6692 0.4481 0.8010
## site24R 0.5854 1.7082 0.4311 0.7951
## site25R 0.7046 1.4193 0.4206 1.1801
## site26R 0.4740 2.1098 0.3012 0.7458
## site27R 0.5696 1.7557 0.2471 1.3127
## site28R 0.4436 2.2543 0.2156 0.9125
## site29R 0.5381 1.8585 0.3386 0.8551
## site30R 0.4029 2.4818 0.1705 0.9521
## site31R 0.5262 1.9003 0.3575 0.7745
## site32R 0.6321 1.5820 0.5531 0.7224
## site33R 0.4973 2.0110 0.3166 0.7811
## site34R 0.5801 1.7238 0.3580 0.9400
## site35R 0.7140 1.4005 0.5856 0.8706
## site36R 0.3960 2.5252 0.1827 0.8581
## site37R 0.4194 2.3841 0.1862 0.9448
## site38R 0.5832 1.7148 0.4599 0.7395
## site39R 0.6516 1.5347 0.5383 0.7887
## site40R 0.5199 1.9234 0.1772 1.5251
## site41R 0.5280 1.8941 0.3381 0.8245
## site42R 0.6422 1.5571 0.3568 1.1559
##
## Concordance= 0.607 (se = 0.04 )
## Likelihood ratio test= 456.5 on 59 df, p=<2e-16
## Wald test = 2.62 on 59 df, p=1
## Score (logrank) test = 432.6 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ BC + dx_yr, data=Simm_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ BC + dx_yr, data = Simm_IPF,
## id = ID)
##
## n= 31982, number of events= 695
## (446 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## BC 0.628259 1.874345 0.210362 2.987 0.00282 **
## dx_yr 0.028760 1.029178 0.008759 3.283 0.00103 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BC 1.874 0.5335 1.241 2.831
## dx_yr 1.029 0.9716 1.012 1.047
##
## Concordance= 0.526 (se = 0.013 )
## Likelihood ratio test= 16.39 on 2 df, p=3e-04
## Wald test = 16.41 on 2 df, p=3e-04
## Score (logrank) test = 16.42 on 2 df, p=3e-04
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ BC + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv, data=Simm_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ BC + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv, data = Simm_IPF, id = ID)
##
## n= 30488, number of events= 670
## (1940 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## BC 0.632992 1.883237 0.219097 2.889 0.00386 **
## dx_yr 0.016479 1.016616 0.009334 1.765 0.07749 .
## age_dx 0.001827 1.001828 0.004544 0.402 0.68766
## sexF -0.369143 0.691327 0.088891 -4.153 3.29e-05 ***
## dich_RaceNon-White 0.156125 1.168973 0.128500 1.215 0.22437
## smokeHxFormer 0.078484 1.081646 0.091931 0.854 0.39326
## smokeHxAlways -0.394385 0.674095 0.266559 -1.480 0.13900
## smokeHxUnknown 0.468359 1.597371 0.170061 2.754 0.00589 **
## disadv 0.411600 1.509231 0.134154 3.068 0.00215 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BC 1.8832 0.5310 1.2258 2.8933
## dx_yr 1.0166 0.9837 0.9982 1.0354
## age_dx 1.0018 0.9982 0.9929 1.0108
## sexF 0.6913 1.4465 0.5808 0.8229
## dich_RaceNon-White 1.1690 0.8555 0.9087 1.5038
## smokeHxFormer 1.0816 0.9245 0.9033 1.2952
## smokeHxAlways 0.6741 1.4835 0.3998 1.1366
## smokeHxUnknown 1.5974 0.6260 1.1446 2.2293
## disadv 1.5092 0.6626 1.1603 1.9631
##
## Concordance= 0.591 (se = 0.012 )
## Likelihood ratio test= 59.85 on 9 df, p=1e-09
## Wald test = 60.57 on 9 df, p=1e-09
## Score (logrank) test = 61.4 on 9 df, p=7e-10
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ BC + dx_yr + site, data=PFF_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ BC + dx_yr + site,
## data = PFF_IPF, id = ID)
##
## n= 54499, number of events= 1146
## (217 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## BC 0.099257 1.104350 0.191189 0.519 0.6037
## dx_yr 0.106755 1.112661 0.014418 7.404 1.32e-13 ***
## site02R 0.646203 1.908281 0.336503 1.920 0.0548 .
## site03R -0.253290 0.776243 0.347464 -0.729 0.4660
## site04R -0.226163 0.797588 0.451087 -0.501 0.6161
## site05R -0.330905 0.718273 0.373927 -0.885 0.3762
## site06R -0.043541 0.957393 0.327802 -0.133 0.8943
## site07R -0.316679 0.728565 0.306205 -1.034 0.3010
## site08R NA NA 0.000000 NA NA
## site09R -0.023325 0.976945 0.340386 -0.069 0.9454
## site10R -0.134990 0.873725 0.385840 -0.350 0.7264
## site11R -0.089347 0.914528 0.306042 -0.292 0.7703
## site12R -0.075371 0.927399 0.334105 -0.226 0.8215
## site13R -0.306567 0.735969 0.304772 -1.006 0.3145
## site14R -0.371014 0.690034 0.642609 -0.577 0.5637
## site15R 0.022444 1.022698 0.332948 0.067 0.9463
## site16R 0.125508 1.133725 0.346084 0.363 0.7169
## site17R 0.021720 1.021957 0.362292 0.060 0.9522
## site18R -0.280953 0.755064 0.323999 -0.867 0.3859
## site19R 0.003931 1.003938 0.350860 0.011 0.9911
## site20R -0.250064 0.778751 0.347470 -0.720 0.4717
## site21R -0.317079 0.728273 0.327771 -0.967 0.3334
## site22R -0.079677 0.923414 0.309275 -0.258 0.7967
## site23R -0.161592 0.850789 0.323082 -0.500 0.6170
## site24R -0.132223 0.876145 0.331025 -0.399 0.6896
## site25R -0.011326 0.988738 0.325267 -0.035 0.9722
## site26R -0.394903 0.673745 0.344604 -1.146 0.2518
## site27R 0.004993 1.005006 0.760579 0.007 0.9948
## site28R -0.142427 0.867251 0.438201 -0.325 0.7452
## site29R -0.280489 0.755414 0.433934 -0.646 0.5180
## site30R -0.229097 0.795251 0.336184 -0.681 0.4956
## site31R -0.322061 0.724654 0.450132 -0.715 0.4743
## site32R -0.351340 0.703745 0.369009 -0.952 0.3410
## site33R -0.334623 0.715608 0.332471 -1.006 0.3142
## site34R -0.139899 0.869446 0.313538 -0.446 0.6555
## site35R -0.105720 0.899676 0.333851 -0.317 0.7515
## site36R -0.219538 0.802889 0.342673 -0.641 0.5217
## site37R -0.183338 0.832486 0.337367 -0.543 0.5868
## site38R -0.291973 0.746789 0.330256 -0.884 0.3767
## site39R -0.210404 0.810257 0.360824 -0.583 0.5598
## site40R 0.085412 1.089165 0.384113 0.222 0.8240
## site41R -0.224165 0.799183 0.372044 -0.603 0.5468
## site42R 0.064179 1.066283 0.362462 0.177 0.8595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BC 1.1043 0.9055 0.7592 1.606
## dx_yr 1.1127 0.8987 1.0817 1.145
## site02R 1.9083 0.5240 0.9868 3.690
## site03R 0.7762 1.2883 0.3929 1.534
## site04R 0.7976 1.2538 0.3295 1.931
## site05R 0.7183 1.3922 0.3451 1.495
## site06R 0.9574 1.0445 0.5036 1.820
## site07R 0.7286 1.3726 0.3998 1.328
## site08R NA NA NA NA
## site09R 0.9769 1.0236 0.5013 1.904
## site10R 0.8737 1.1445 0.4102 1.861
## site11R 0.9145 1.0935 0.5020 1.666
## site12R 0.9274 1.0783 0.4818 1.785
## site13R 0.7360 1.3588 0.4050 1.337
## site14R 0.6900 1.4492 0.1958 2.431
## site15R 1.0227 0.9778 0.5325 1.964
## site16R 1.1337 0.8820 0.5753 2.234
## site17R 1.0220 0.9785 0.5024 2.079
## site18R 0.7551 1.3244 0.4001 1.425
## site19R 1.0039 0.9961 0.5047 1.997
## site20R 0.7788 1.2841 0.3941 1.539
## site21R 0.7283 1.3731 0.3831 1.385
## site22R 0.9234 1.0829 0.5037 1.693
## site23R 0.8508 1.1754 0.4517 1.603
## site24R 0.8761 1.1414 0.4579 1.676
## site25R 0.9887 1.0114 0.5227 1.870
## site26R 0.6737 1.4842 0.3429 1.324
## site27R 1.0050 0.9950 0.2263 4.462
## site28R 0.8673 1.1531 0.3674 2.047
## site29R 0.7554 1.3238 0.3227 1.768
## site30R 0.7953 1.2575 0.4115 1.537
## site31R 0.7247 1.3800 0.2999 1.751
## site32R 0.7037 1.4210 0.3414 1.450
## site33R 0.7156 1.3974 0.3730 1.373
## site34R 0.8694 1.1502 0.4703 1.607
## site35R 0.8997 1.1115 0.4676 1.731
## site36R 0.8029 1.2455 0.4102 1.572
## site37R 0.8325 1.2012 0.4297 1.613
## site38R 0.7468 1.3391 0.3909 1.427
## site39R 0.8103 1.2342 0.3995 1.643
## site40R 1.0892 0.9181 0.5130 2.312
## site41R 0.7992 1.2513 0.3854 1.657
## site42R 1.0663 0.9378 0.5240 2.170
##
## Concordance= 0.59 (se = 0.009 )
## Likelihood ratio test= 110.8 on 42 df, p=4e-08
## Wald test = 109.1 on 42 df, p=7e-08
## Score (logrank) test = 113.3 on 42 df, p=2e-08
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ BC + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=PFF_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ BC + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = PFF_IPF,
## id = ID)
##
## n= 53867, number of events= 1131
## (849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## BC 0.1218474 1.1295818 0.1955235 0.623 0.5332
## dx_yr 0.1061118 1.1119462 0.0149235 7.110 1.16e-12 ***
## age_dx -0.0003375 0.9996625 0.0039364 -0.086 0.9317
## sexM 0.0616244 1.0635628 0.0712850 0.864 0.3873
## dich_RaceNon-White -0.0620134 0.9398703 0.1261843 -0.491 0.6231
## smokeHxEver 0.0429746 1.0439114 0.0646559 0.665 0.5063
## disadv -0.0578564 0.9437854 0.1124418 -0.515 0.6069
## site02R 0.5909210 1.8056506 0.3495292 1.691 0.0909 .
## site03R -0.2872648 0.7503131 0.3602265 -0.797 0.4252
## site04R -0.2878085 0.7499052 0.4624276 -0.622 0.5337
## site05R -0.3840169 0.6811199 0.3877908 -0.990 0.3220
## site06R -0.0857503 0.9178234 0.3443273 -0.249 0.8033
## site07R -0.3646471 0.6944417 0.3210754 -1.136 0.2561
## site08R NA NA 0.0000000 NA NA
## site09R -0.1088370 0.8968766 0.3555378 -0.306 0.7595
## site10R -0.1912467 0.8259288 0.3987437 -0.480 0.6315
## site11R -0.1551920 0.8562508 0.3257545 -0.476 0.6338
## site12R -0.1205837 0.8864029 0.3476512 -0.347 0.7287
## site13R -0.3708642 0.6901376 0.3194745 -1.161 0.2457
## site14R -0.4411488 0.6432970 0.6508203 -0.678 0.4979
## site15R -0.0316308 0.9688642 0.3494681 -0.091 0.9279
## site16R 0.0743534 1.0771874 0.3584692 0.207 0.8357
## site17R -0.0420303 0.9588407 0.3767215 -0.112 0.9112
## site18R -0.3196326 0.7264159 0.3400481 -0.940 0.3472
## site19R -0.0418402 0.9590230 0.3627934 -0.115 0.9082
## site20R -0.3058402 0.7365043 0.3616359 -0.846 0.3977
## site21R -0.3700646 0.6906897 0.3433973 -1.078 0.2812
## site22R -0.1409431 0.8685387 0.3251488 -0.433 0.6647
## site23R -0.2292489 0.7951306 0.3392161 -0.676 0.4992
## site24R -0.1899407 0.8270082 0.3445719 -0.551 0.5815
## site25R -0.0462749 0.9547795 0.3385172 -0.137 0.8913
## site26R -0.4296493 0.6507373 0.3575209 -1.202 0.2295
## site27R -0.1097219 0.8960833 0.7722682 -0.142 0.8870
## site28R -0.1818867 0.8336958 0.4508562 -0.403 0.6866
## site29R -0.3386558 0.7127277 0.4456247 -0.760 0.4473
## site30R -0.2920598 0.7467239 0.3507292 -0.833 0.4050
## site31R -0.3775256 0.6855556 0.4597307 -0.821 0.4115
## site32R -0.4175668 0.6586475 0.3917752 -1.066 0.2865
## site33R -0.3768800 0.6859984 0.3481573 -1.082 0.2790
## site34R -0.1856758 0.8305428 0.3289784 -0.564 0.5725
## site35R -0.1691685 0.8443666 0.3483704 -0.486 0.6273
## site36R -0.2596634 0.7713112 0.3584497 -0.724 0.4688
## site37R -0.2340428 0.7913279 0.3514456 -0.666 0.5054
## site38R -0.3344544 0.7157285 0.3445620 -0.971 0.3317
## site39R -0.2666567 0.7659360 0.3740019 -0.713 0.4759
## site40R 0.0332337 1.0337921 0.3995037 0.083 0.9337
## site41R -0.2701151 0.7632916 0.3836329 -0.704 0.4814
## site42R -0.0092087 0.9908336 0.3759002 -0.024 0.9805
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BC 1.1296 0.8853 0.7700 1.657
## dx_yr 1.1119 0.8993 1.0799 1.145
## age_dx 0.9997 1.0003 0.9920 1.007
## sexM 1.0636 0.9402 0.9249 1.223
## dich_RaceNon-White 0.9399 1.0640 0.7339 1.204
## smokeHxEver 1.0439 0.9579 0.9197 1.185
## disadv 0.9438 1.0596 0.7571 1.176
## site02R 1.8057 0.5538 0.9102 3.582
## site03R 0.7503 1.3328 0.3704 1.520
## site04R 0.7499 1.3335 0.3030 1.856
## site05R 0.6811 1.4682 0.3185 1.457
## site06R 0.9178 1.0895 0.4674 1.802
## site07R 0.6944 1.4400 0.3701 1.303
## site08R NA NA NA NA
## site09R 0.8969 1.1150 0.4468 1.800
## site10R 0.8259 1.2108 0.3780 1.804
## site11R 0.8563 1.1679 0.4522 1.621
## site12R 0.8864 1.1282 0.4484 1.752
## site13R 0.6901 1.4490 0.3690 1.291
## site14R 0.6433 1.5545 0.1797 2.304
## site15R 0.9689 1.0321 0.4884 1.922
## site16R 1.0772 0.9283 0.5335 2.175
## site17R 0.9588 1.0429 0.4582 2.006
## site18R 0.7264 1.3766 0.3730 1.415
## site19R 0.9590 1.0427 0.4710 1.953
## site20R 0.7365 1.3578 0.3625 1.496
## site21R 0.6907 1.4478 0.3524 1.354
## site22R 0.8685 1.1514 0.4592 1.643
## site23R 0.7951 1.2577 0.4090 1.546
## site24R 0.8270 1.2092 0.4209 1.625
## site25R 0.9548 1.0474 0.4918 1.854
## site26R 0.6507 1.5367 0.3229 1.311
## site27R 0.8961 1.1160 0.1972 4.071
## site28R 0.8337 1.1995 0.3445 2.017
## site29R 0.7127 1.4031 0.2976 1.707
## site30R 0.7467 1.3392 0.3755 1.485
## site31R 0.6856 1.4587 0.2784 1.688
## site32R 0.6586 1.5183 0.3056 1.419
## site33R 0.6860 1.4577 0.3467 1.357
## site34R 0.8305 1.2040 0.4358 1.583
## site35R 0.8444 1.1843 0.4266 1.671
## site36R 0.7713 1.2965 0.3820 1.557
## site37R 0.7913 1.2637 0.3974 1.576
## site38R 0.7157 1.3972 0.3643 1.406
## site39R 0.7659 1.3056 0.3680 1.594
## site40R 1.0338 0.9673 0.4725 2.262
## site41R 0.7633 1.3101 0.3599 1.619
## site42R 0.9908 1.0093 0.4743 2.070
##
## Concordance= 0.592 (se = 0.009 )
## Likelihood ratio test= 111.6 on 47 df, p=4e-07
## Wald test = 110.2 on 47 df, p=5e-07
## Score (logrank) test = 114.5 on 47 df, p=1e-07
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ BC + dx_yr + site, data=CARE_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ BC + dx_yr + site,
## data = CARE_IPF, id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## BC -0.22373 0.79953 0.21147 -1.058 0.29007
## dx_yr 0.81648 2.26252 0.03239 25.209 < 2e-16 ***
## site102 0.19599 1.21651 0.14898 1.315 0.18835
## site103 0.50566 1.65808 0.12013 4.209 2.56e-05 ***
## site104 0.36060 1.43418 0.13978 2.580 0.00989 **
## site105 0.05329 1.05474 0.12653 0.421 0.67363
## site106 0.14127 1.15173 0.12046 1.173 0.24088
## site107 0.09373 1.09827 0.19780 0.474 0.63559
## site108 -0.43347 0.64826 0.26575 -1.631 0.10286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BC 0.7995 1.2507 0.5282 1.210
## dx_yr 2.2625 0.4420 2.1234 2.411
## site102 1.2165 0.8220 0.9084 1.629
## site103 1.6581 0.6031 1.3103 2.098
## site104 1.4342 0.6973 1.0905 1.886
## site105 1.0547 0.9481 0.8231 1.352
## site106 1.1517 0.8683 0.9095 1.458
## site107 1.0983 0.9105 0.7453 1.618
## site108 0.6483 1.5426 0.3851 1.091
##
## Concordance= 0.774 (se = 0.01 )
## Likelihood ratio test= 1053 on 9 df, p=<2e-16
## Wald test = 665.1 on 9 df, p=<2e-16
## Score (logrank) test = 588.6 on 9 df, p=<2e-16
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ BC + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=CARE_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ BC + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = CARE_IPF,
## id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## BC -0.271875 0.761950 0.216355 -1.257 0.20889
## dx_yr 0.816520 2.262612 0.032570 25.070 < 2e-16 ***
## age_dx 0.012385 1.012462 0.004511 2.745 0.00605 **
## sexF -0.043440 0.957490 0.077028 -0.564 0.57279
## dich_RaceNon-White -0.139022 0.870209 0.110288 -1.261 0.20748
## smokeHxFormer 0.002227 1.002229 0.081503 0.027 0.97820
## smokeHxAlways 0.051606 1.052961 0.168804 0.306 0.75982
## smokeHxUnknown 0.406790 1.501988 0.734622 0.554 0.57976
## disadv 0.001681 1.001683 0.130506 0.013 0.98972
## site102 0.173283 1.189203 0.151316 1.145 0.25214
## site103 0.517544 1.677902 0.123027 4.207 2.59e-05 ***
## site104 0.366238 1.442298 0.140536 2.606 0.00916 **
## site105 0.069771 1.072263 0.129673 0.538 0.59054
## site106 0.113163 1.119815 0.121638 0.930 0.35220
## site107 0.028870 1.029291 0.205325 0.141 0.88818
## site108 -0.440094 0.643976 0.269385 -1.634 0.10232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BC 0.7619 1.3124 0.4986 1.164
## dx_yr 2.2626 0.4420 2.1227 2.412
## age_dx 1.0125 0.9877 1.0035 1.021
## sexF 0.9575 1.0444 0.8233 1.114
## dich_RaceNon-White 0.8702 1.1491 0.7010 1.080
## smokeHxFormer 1.0022 0.9978 0.8543 1.176
## smokeHxAlways 1.0530 0.9497 0.7564 1.466
## smokeHxUnknown 1.5020 0.6658 0.3559 6.338
## disadv 1.0017 0.9983 0.7756 1.294
## site102 1.1892 0.8409 0.8840 1.600
## site103 1.6779 0.5960 1.3184 2.135
## site104 1.4423 0.6933 1.0950 1.900
## site105 1.0723 0.9326 0.8316 1.383
## site106 1.1198 0.8930 0.8823 1.421
## site107 1.0293 0.9715 0.6883 1.539
## site108 0.6440 1.5529 0.3798 1.092
##
## Concordance= 0.776 (se = 0.01 )
## Likelihood ratio test= 1065 on 16 df, p=<2e-16
## Wald test = 672 on 16 df, p=<2e-16
## Score (logrank) test = 596.7 on 16 df, p=<2e-16
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ BC + dx_yr + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ BC + dx_yr + site,
## data = All_IPF, id = ID, cluster = cohort)
##
## n= 123367, number of events= 2749
## (723 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## BC 0.408909 1.505174 0.116883 0.159890 2.557 0.010545 *
## dx_yr 0.109609 1.115842 0.007103 0.076699 1.429 0.152979
## site02R 0.625110 1.868451 0.334563 0.078830 7.930 2.19e-15 ***
## site03R -0.312166 0.731860 0.347266 0.036797 -8.483 < 2e-16 ***
## site04R -0.299877 0.740909 0.449878 0.097508 -3.075 0.002102 **
## site05R -0.462887 0.629464 0.370487 0.070489 -6.567 5.14e-11 ***
## site06R -0.138325 0.870815 0.325963 0.081221 -1.703 0.088553 .
## site07R -0.376194 0.686469 0.305101 0.109113 -3.448 0.000565 ***
## site09R -0.081960 0.921309 0.339988 0.032784 -2.500 0.012419 *
## site1 0.117649 1.124849 0.283576 0.107991 1.089 0.275962
## site101 -0.130442 0.877708 0.293320 0.140283 -0.930 0.352451
## site102 -0.207992 0.812213 0.298007 0.101098 -2.057 0.039655 *
## site103 0.119517 1.126952 0.286364 0.080669 1.482 0.138454
## site104 -0.174801 0.839624 0.294721 0.083322 -2.098 0.035914 *
## site105 0.082066 1.085528 0.289585 0.141993 0.578 0.563293
## site106 -0.265428 0.766878 0.286713 0.103167 -2.573 0.010088 *
## site107 0.215669 1.240691 0.326760 0.154675 1.394 0.163217
## site108 0.188009 1.206845 0.369696 0.196000 0.959 0.337444
## site10R -0.103499 0.901677 0.385491 0.057116 -1.812 0.069972 .
## site11R -0.190240 0.826761 0.301585 0.087523 -2.174 0.029736 *
## site12R -0.084005 0.919427 0.333912 0.029152 -2.882 0.003957 **
## site13R -0.379882 0.683942 0.302630 0.111507 -3.407 0.000657 ***
## site14R -0.468043 0.626227 0.641402 0.126051 -3.713 0.000205 ***
## site15R -0.050746 0.950520 0.331279 0.038778 -1.309 0.190667
## site16R 0.065525 1.067719 0.345034 0.041103 1.594 0.110903
## site17R -0.045170 0.955835 0.360760 0.034077 -1.326 0.184992
## site18R -0.317852 0.727711 0.321967 0.161086 -1.973 0.048475 *
## site19R 0.021805 1.022044 0.350239 0.009115 2.392 0.016749 *
## site20R -0.303856 0.737967 0.347186 0.030297 -10.029 < 2e-16 ***
## site21R -0.467824 0.626363 0.322356 0.094747 -4.938 7.91e-07 ***
## site22R -0.179265 0.835884 0.306645 0.052928 -3.387 0.000707 ***
## site23R -0.207165 0.812885 0.322648 0.028442 -7.284 3.25e-13 ***
## site24R -0.160489 0.851727 0.330657 0.055043 -2.916 0.003549 **
## site25R -0.049299 0.951896 0.324959 0.050176 -0.983 0.325844
## site26R -0.415223 0.660193 0.344468 0.077119 -5.384 7.28e-08 ***
## site27R -0.083809 0.919607 0.759895 0.125006 -0.670 0.502580
## site28R -0.276293 0.758590 0.435366 0.068437 -4.037 5.41e-05 ***
## site29R -0.332769 0.716936 0.433757 0.050387 -6.604 4.00e-11 ***
## site30R -0.334010 0.716047 0.332289 0.126003 -2.651 0.008030 **
## site31R -0.384518 0.680779 0.449616 0.075222 -5.112 3.19e-07 ***
## site32R -0.395489 0.673351 0.368694 0.040058 -9.873 < 2e-16 ***
## site33R -0.371889 0.689431 0.332191 0.085997 -4.324 1.53e-05 ***
## site34R -0.170611 0.843149 0.313449 0.041747 -4.087 4.37e-05 ***
## site35R -0.122969 0.884291 0.333805 0.027042 -4.547 5.43e-06 ***
## site36R -0.344904 0.708289 0.339454 0.065747 -5.246 1.56e-07 ***
## site37R -0.286127 0.751167 0.335069 0.073930 -3.870 0.000109 ***
## site38R -0.358490 0.698731 0.329367 0.048223 -7.434 1.05e-13 ***
## site39R -0.275219 0.759406 0.360436 0.039343 -6.995 2.65e-12 ***
## site40R -0.200190 0.818575 0.359432 0.206760 -0.968 0.332930
## site41R -0.321460 0.725090 0.369764 0.067069 -4.793 1.64e-06 ***
## site42R -0.004633 0.995378 0.360765 0.046262 -0.100 0.920229
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BC 1.5052 0.6644 1.1002 2.0591
## dx_yr 1.1158 0.8962 0.9601 1.2968
## site02R 1.8685 0.5352 1.6010 2.1806
## site03R 0.7319 1.3664 0.6809 0.7866
## site04R 0.7409 1.3497 0.6120 0.8969
## site05R 0.6295 1.5887 0.5482 0.7227
## site06R 0.8708 1.1483 0.7427 1.0211
## site07R 0.6865 1.4567 0.5543 0.8502
## site09R 0.9213 1.0854 0.8640 0.9825
## site1 1.1248 0.8890 0.9103 1.3900
## site101 0.8777 1.1393 0.6667 1.1555
## site102 0.8122 1.2312 0.6662 0.9902
## site103 1.1270 0.8873 0.9621 1.3200
## site104 0.8396 1.1910 0.7131 0.9886
## site105 1.0855 0.9212 0.8218 1.4339
## site106 0.7669 1.3040 0.6265 0.9387
## site107 1.2407 0.8060 0.9162 1.6801
## site108 1.2068 0.8286 0.8219 1.7721
## site10R 0.9017 1.1090 0.8062 1.0085
## site11R 0.8268 1.2095 0.6964 0.9815
## site12R 0.9194 1.0876 0.8684 0.9735
## site13R 0.6839 1.4621 0.5497 0.8510
## site14R 0.6262 1.5969 0.4891 0.8017
## site15R 0.9505 1.0521 0.8810 1.0256
## site16R 1.0677 0.9366 0.9851 1.1573
## site17R 0.9558 1.0462 0.8941 1.0219
## site18R 0.7277 1.3742 0.5307 0.9979
## site19R 1.0220 0.9784 1.0039 1.0405
## site20R 0.7380 1.3551 0.6954 0.7831
## site21R 0.6264 1.5965 0.5202 0.7542
## site22R 0.8359 1.1963 0.7535 0.9273
## site23R 0.8129 1.2302 0.7688 0.8595
## site24R 0.8517 1.1741 0.7646 0.9488
## site25R 0.9519 1.0505 0.8627 1.0503
## site26R 0.6602 1.5147 0.5676 0.7679
## site27R 0.9196 1.0874 0.7198 1.1749
## site28R 0.7586 1.3182 0.6634 0.8675
## site29R 0.7169 1.3948 0.6495 0.7914
## site30R 0.7160 1.3966 0.5594 0.9166
## site31R 0.6808 1.4689 0.5875 0.7889
## site32R 0.6734 1.4851 0.6225 0.7283
## site33R 0.6894 1.4505 0.5825 0.8160
## site34R 0.8431 1.1860 0.7769 0.9150
## site35R 0.8843 1.1308 0.8386 0.9324
## site36R 0.7083 1.4119 0.6227 0.8057
## site37R 0.7512 1.3313 0.6498 0.8683
## site38R 0.6987 1.4312 0.6357 0.7680
## site39R 0.7594 1.3168 0.7030 0.8203
## site40R 0.8186 1.2216 0.5458 1.2276
## site41R 0.7251 1.3791 0.6358 0.8270
## site42R 0.9954 1.0046 0.9091 1.0898
##
## Concordance= 0.596 (se = 0.042 )
## Likelihood ratio test= 360 on 51 df, p=<2e-16
## Wald test = 6.69 on 51 df, p=1
## Score (logrank) test = 349.8 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ BC + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ BC + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = All_IPF,
## id = ID, cluster = cohort)
##
## n= 121241, number of events= 2709
## (2849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## BC 0.408936 1.505215 0.119460 0.168224 2.431 0.015061 *
## dx_yr 0.106752 1.112659 0.007493 0.082569 1.293 0.196047
## age_dx 0.003889 1.003896 0.002446 0.001895 2.052 0.040125 *
## sexF -0.154513 0.856832 0.044855 0.082701 -1.868 0.061714 .
## dich_RaceNon-White 0.012831 1.012914 0.068754 0.062744 0.204 0.837965
## smokeHxFormer 0.101466 1.106792 0.059347 0.032842 3.089 0.002005 **
## smokeHxAlways -0.029455 0.970975 0.138344 0.109023 -0.270 0.787027
## smokeHxUnknown 0.284492 1.329086 0.154015 0.241709 1.177 0.239196
## smokeHxEver 0.036456 1.037129 0.064320 0.008009 4.552 5.32e-06 ***
## disadv 0.025355 1.025679 0.070567 0.143259 0.177 0.859520
## site02R 0.590435 1.804773 0.345515 0.107672 5.484 4.17e-08 ***
## site03R -0.311144 0.732609 0.357692 0.043370 -7.174 7.27e-13 ***
## site04R -0.316361 0.728797 0.458543 0.132193 -2.393 0.016704 *
## site05R -0.478389 0.619781 0.381622 0.067784 -7.058 1.69e-12 ***
## site06R -0.165352 0.847595 0.339043 0.088767 -1.863 0.062494 .
## site07R -0.402471 0.668666 0.317079 0.120541 -3.339 0.000841 ***
## site09R -0.150497 0.860280 0.352699 0.031104 -4.839 1.31e-06 ***
## site1 0.031751 1.032261 0.300532 0.117353 0.271 0.786727
## site101 -0.202370 0.816793 0.308489 0.139190 -1.454 0.145970
## site102 -0.299791 0.740973 0.313616 0.109857 -2.729 0.006354 **
## site103 0.025321 1.025644 0.301884 0.053204 0.476 0.634134
## site104 -0.258297 0.772366 0.310474 0.062390 -4.140 3.47e-05 ***
## site105 0.002697 1.002701 0.306691 0.137500 0.020 0.984348
## site106 -0.364439 0.694586 0.302984 0.100196 -3.637 0.000276 ***
## site107 0.105752 1.111546 0.342348 0.137555 0.769 0.442015
## site108 0.095608 1.100328 0.381919 0.179467 0.533 0.594217
## site10R -0.183664 0.832216 0.396191 0.031993 -5.741 9.42e-09 ***
## site11R -0.241137 0.785734 0.316600 0.091484 -2.636 0.008393 **
## site12R -0.122088 0.885070 0.345083 0.029585 -4.127 3.68e-05 ***
## site13R -0.437616 0.645574 0.314763 0.130048 -3.365 0.000765 ***
## site14R -0.499207 0.607012 0.647570 0.070585 -7.072 1.52e-12 ***
## site15R -0.105749 0.899651 0.343850 0.036300 -2.913 0.003577 **
## site16R 0.046339 1.047429 0.355574 0.022563 2.054 0.039995 *
## site17R -0.076516 0.926338 0.372000 0.034609 -2.211 0.027043 *
## site18R -0.363720 0.695086 0.334228 0.146910 -2.476 0.013293 *
## site19R -0.038922 0.961826 0.360840 0.019815 -1.964 0.049500 *
## site20R -0.321442 0.725103 0.358697 0.028304 -11.357 < 2e-16 ***
## site21R -0.521575 0.593585 0.334488 0.099838 -5.224 1.75e-07 ***
## site22R -0.210279 0.810358 0.319251 0.060829 -3.457 0.000546 ***
## site23R -0.282112 0.754189 0.336628 0.027822 -10.140 < 2e-16 ***
## site24R -0.192360 0.825010 0.342350 0.048542 -3.963 7.41e-05 ***
## site25R -0.081533 0.921702 0.336947 0.077658 -1.050 0.293762
## site26R -0.469183 0.625513 0.355377 0.057682 -8.134 4.16e-16 ***
## site27R -0.097607 0.907005 0.767527 0.158219 -0.617 0.537294
## site28R -0.292598 0.746322 0.444601 0.059515 -4.916 8.82e-07 ***
## site29R -0.359068 0.698327 0.443154 0.092760 -3.871 0.000108 ***
## site30R -0.415753 0.659843 0.345074 0.138295 -3.006 0.002645 **
## site31R -0.438249 0.645165 0.458039 0.080206 -5.464 4.65e-08 ***
## site32R -0.468130 0.626172 0.389383 0.047394 -9.877 < 2e-16 ***
## site33R -0.422899 0.655145 0.345442 0.067293 -6.284 3.29e-10 ***
## site34R -0.203630 0.815764 0.326262 0.051157 -3.980 6.88e-05 ***
## site35R -0.193826 0.823801 0.347008 0.027035 -7.169 7.54e-13 ***
## site36R -0.367252 0.692635 0.351227 0.061994 -5.924 3.14e-09 ***
## site37R -0.320615 0.725703 0.346459 0.090744 -3.533 0.000411 ***
## site38R -0.382264 0.682315 0.340950 0.039546 -9.666 < 2e-16 ***
## site39R -0.336650 0.714159 0.371390 0.042272 -7.964 1.67e-15 ***
## site40R -0.258723 0.772037 0.371214 0.220692 -1.172 0.241065
## site41R -0.377377 0.685657 0.379526 0.074499 -5.066 4.07e-07 ***
## site42R -0.050760 0.950507 0.371424 0.063214 -0.803 0.421990
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## BC 1.5052 0.6644 1.0824 2.0931
## dx_yr 1.1127 0.8987 0.9464 1.3081
## age_dx 1.0039 0.9961 1.0002 1.0076
## sexF 0.8568 1.1671 0.7286 1.0076
## dich_RaceNon-White 1.0129 0.9873 0.8957 1.1455
## smokeHxFormer 1.1068 0.9035 1.0378 1.1804
## smokeHxAlways 0.9710 1.0299 0.7842 1.2023
## smokeHxUnknown 1.3291 0.7524 0.8276 2.1345
## smokeHxEver 1.0371 0.9642 1.0210 1.0535
## disadv 1.0257 0.9750 0.7746 1.3582
## site02R 1.8048 0.5541 1.4614 2.2288
## site03R 0.7326 1.3650 0.6729 0.7976
## site04R 0.7288 1.3721 0.5624 0.9443
## site05R 0.6198 1.6135 0.5427 0.7078
## site06R 0.8476 1.1798 0.7122 1.0087
## site07R 0.6687 1.4955 0.5280 0.8469
## site09R 0.8603 1.1624 0.8094 0.9144
## site1 1.0323 0.9687 0.8202 1.2992
## site101 0.8168 1.2243 0.6218 1.0730
## site102 0.7410 1.3496 0.5974 0.9190
## site103 1.0256 0.9750 0.9241 1.1384
## site104 0.7724 1.2947 0.6835 0.8728
## site105 1.0027 0.9973 0.7658 1.3128
## site106 0.6946 1.4397 0.5707 0.8453
## site107 1.1115 0.8996 0.8489 1.4555
## site108 1.1003 0.9088 0.7740 1.5642
## site10R 0.8322 1.2016 0.7816 0.8861
## site11R 0.7857 1.2727 0.6568 0.9400
## site12R 0.8851 1.1299 0.8352 0.9379
## site13R 0.6456 1.5490 0.5003 0.8330
## site14R 0.6070 1.6474 0.5286 0.6971
## site15R 0.8997 1.1115 0.8379 0.9660
## site16R 1.0474 0.9547 1.0021 1.0948
## site17R 0.9263 1.0795 0.8656 0.9914
## site18R 0.6951 1.4387 0.5212 0.9270
## site19R 0.9618 1.0397 0.9252 0.9999
## site20R 0.7251 1.3791 0.6860 0.7665
## site21R 0.5936 1.6847 0.4881 0.7219
## site22R 0.8104 1.2340 0.7193 0.9130
## site23R 0.7542 1.3259 0.7142 0.7965
## site24R 0.8250 1.2121 0.7501 0.9074
## site25R 0.9217 1.0849 0.7916 1.0732
## site26R 0.6255 1.5987 0.5586 0.7004
## site27R 0.9070 1.1025 0.6652 1.2368
## site28R 0.7463 1.3399 0.6642 0.8387
## site29R 0.6983 1.4320 0.5822 0.8376
## site30R 0.6598 1.5155 0.5032 0.8653
## site31R 0.6452 1.5500 0.5513 0.7550
## site32R 0.6262 1.5970 0.5706 0.6871
## site33R 0.6551 1.5264 0.5742 0.7475
## site34R 0.8158 1.2258 0.7379 0.9018
## site35R 0.8238 1.2139 0.7813 0.8686
## site36R 0.6926 1.4438 0.6134 0.7821
## site37R 0.7257 1.3780 0.6075 0.8670
## site38R 0.6823 1.4656 0.6314 0.7373
## site39R 0.7142 1.4002 0.6574 0.7758
## site40R 0.7720 1.2953 0.5009 1.1898
## site41R 0.6857 1.4585 0.5925 0.7935
## site42R 0.9505 1.0521 0.8397 1.0759
##
## Concordance= 0.6 (se = 0.039 )
## Likelihood ratio test= 391.5 on 59 df, p=<2e-16
## Wald test = 5.99 on 59 df, p=1
## Score (logrank) test = 386.8 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ OM + dx_yr, data=Simm_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ OM + dx_yr, data = Simm_IPF,
## id = ID)
##
## n= 31982, number of events= 695
## (446 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## OM 0.178291 1.195173 0.053759 3.316 0.000912 ***
## dx_yr 0.028757 1.029174 0.008606 3.342 0.000833 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## OM 1.195 0.8367 1.076 1.328
## dx_yr 1.029 0.9717 1.012 1.047
##
## Concordance= 0.541 (se = 0.013 )
## Likelihood ratio test= 18.24 on 2 df, p=1e-04
## Wald test = 18.76 on 2 df, p=8e-05
## Score (logrank) test = 18.79 on 2 df, p=8e-05
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ OM + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv, data=Simm_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ OM + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv, data = Simm_IPF, id = ID)
##
## n= 30488, number of events= 670
## (1940 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## OM 0.168922 1.184027 0.056134 3.009 0.00262 **
## dx_yr 0.016027 1.016156 0.009194 1.743 0.08131 .
## age_dx 0.002990 1.002995 0.004558 0.656 0.51185
## sexF -0.365703 0.693708 0.088955 -4.111 3.94e-05 ***
## dich_RaceNon-White 0.137471 1.147369 0.128716 1.068 0.28551
## smokeHxFormer 0.077953 1.081072 0.091881 0.848 0.39621
## smokeHxAlways -0.441039 0.643368 0.266488 -1.655 0.09792 .
## smokeHxUnknown 0.454587 1.575522 0.170628 2.664 0.00772 **
## disadv 0.390925 1.478348 0.133259 2.934 0.00335 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## OM 1.1840 0.8446 1.0607 1.3217
## dx_yr 1.0162 0.9841 0.9980 1.0346
## age_dx 1.0030 0.9970 0.9941 1.0120
## sexF 0.6937 1.4415 0.5827 0.8258
## dich_RaceNon-White 1.1474 0.8716 0.8915 1.4766
## smokeHxFormer 1.0811 0.9250 0.9029 1.2944
## smokeHxAlways 0.6434 1.5543 0.3816 1.0847
## smokeHxUnknown 1.5755 0.6347 1.1277 2.2012
## disadv 1.4783 0.6764 1.1385 1.9196
##
## Concordance= 0.596 (se = 0.012 )
## Likelihood ratio test= 60.46 on 9 df, p=1e-09
## Wald test = 61.74 on 9 df, p=6e-10
## Score (logrank) test = 62.65 on 9 df, p=4e-10
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ OM + dx_yr + site, data=PFF_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ OM + dx_yr + site,
## data = PFF_IPF, id = ID)
##
## n= 54499, number of events= 1146
## (217 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## OM 0.026811 1.027173 0.040359 0.664 0.5065
## dx_yr 0.105067 1.110785 0.013605 7.723 1.14e-14 ***
## site02R 0.644361 1.904770 0.336296 1.916 0.0554 .
## site03R -0.249479 0.779207 0.347173 -0.719 0.4724
## site04R -0.202282 0.816865 0.449609 -0.450 0.6528
## site05R -0.320918 0.725483 0.370195 -0.867 0.3860
## site06R -0.034171 0.966407 0.325679 -0.105 0.9164
## site07R -0.306329 0.736145 0.305021 -1.004 0.3152
## site08R NA NA 0.000000 NA NA
## site09R -0.012780 0.987301 0.339751 -0.038 0.9700
## site10R -0.115701 0.890742 0.387975 -0.298 0.7655
## site11R -0.089278 0.914591 0.303396 -0.294 0.7686
## site12R -0.061396 0.940451 0.335225 -0.183 0.8547
## site13R -0.287192 0.750368 0.302020 -0.951 0.3417
## site14R -0.344690 0.708440 0.641282 -0.538 0.5909
## site15R 0.029671 1.030116 0.331072 0.090 0.9286
## site16R 0.143857 1.154719 0.344433 0.418 0.6762
## site17R 0.031972 1.032489 0.360320 0.089 0.9293
## site18R -0.277546 0.757641 0.322995 -0.859 0.3902
## site19R 0.019901 1.020100 0.352746 0.056 0.9550
## site20R -0.238660 0.787683 0.347272 -0.687 0.4919
## site21R -0.305290 0.736910 0.321809 -0.949 0.3428
## site22R -0.052924 0.948452 0.305140 -0.173 0.8623
## site23R -0.156379 0.855235 0.322545 -0.485 0.6278
## site24R -0.118150 0.888563 0.331982 -0.356 0.7219
## site25R -0.007785 0.992245 0.325029 -0.024 0.9809
## site26R -0.382687 0.682027 0.345011 -1.109 0.2673
## site27R 0.028110 1.028509 0.759854 0.037 0.9705
## site28R -0.127422 0.880362 0.434516 -0.293 0.7693
## site29R -0.259591 0.771367 0.434311 -0.598 0.5500
## site30R -0.202961 0.816310 0.330850 -0.613 0.5396
## site31R -0.305501 0.736754 0.449542 -0.680 0.4968
## site32R -0.339344 0.712237 0.368543 -0.921 0.3572
## site33R -0.323360 0.723713 0.332404 -0.973 0.3307
## site34R -0.127196 0.880561 0.313871 -0.405 0.6853
## site35R -0.098211 0.906458 0.333947 -0.294 0.7687
## site36R -0.208571 0.811744 0.338918 -0.615 0.5383
## site37R -0.162521 0.849998 0.333961 -0.487 0.6265
## site38R -0.281466 0.754677 0.329039 -0.855 0.3923
## site39R -0.201714 0.817329 0.360102 -0.560 0.5754
## site40R 0.106452 1.112324 0.357942 0.297 0.7662
## site41R -0.203641 0.815755 0.368610 -0.552 0.5806
## site42R 0.086119 1.089936 0.359994 0.239 0.8109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## OM 1.0272 0.9735 0.9491 1.112
## dx_yr 1.1108 0.9003 1.0816 1.141
## site02R 1.9048 0.5250 0.9853 3.682
## site03R 0.7792 1.2834 0.3946 1.539
## site04R 0.8169 1.2242 0.3384 1.972
## site05R 0.7255 1.3784 0.3512 1.499
## site06R 0.9664 1.0348 0.5104 1.830
## site07R 0.7361 1.3584 0.4049 1.338
## site08R NA NA NA NA
## site09R 0.9873 1.0129 0.5073 1.922
## site10R 0.8907 1.1227 0.4164 1.905
## site11R 0.9146 1.0934 0.5046 1.658
## site12R 0.9405 1.0633 0.4875 1.814
## site13R 0.7504 1.3327 0.4151 1.356
## site14R 0.7084 1.4116 0.2016 2.490
## site15R 1.0301 0.9708 0.5384 1.971
## site16R 1.1547 0.8660 0.5879 2.268
## site17R 1.0325 0.9685 0.5095 2.092
## site18R 0.7576 1.3199 0.4023 1.427
## site19R 1.0201 0.9803 0.5110 2.037
## site20R 0.7877 1.2695 0.3988 1.556
## site21R 0.7369 1.3570 0.3922 1.385
## site22R 0.9485 1.0543 0.5215 1.725
## site23R 0.8552 1.1693 0.4545 1.609
## site24R 0.8886 1.1254 0.4636 1.703
## site25R 0.9922 1.0078 0.5248 1.876
## site26R 0.6820 1.4662 0.3468 1.341
## site27R 1.0285 0.9723 0.2320 4.560
## site28R 0.8804 1.1359 0.3757 2.063
## site29R 0.7714 1.2964 0.3293 1.807
## site30R 0.8163 1.2250 0.4268 1.561
## site31R 0.7368 1.3573 0.3053 1.778
## site32R 0.7122 1.4040 0.3459 1.467
## site33R 0.7237 1.3818 0.3772 1.388
## site34R 0.8806 1.1356 0.4760 1.629
## site35R 0.9065 1.1032 0.4711 1.744
## site36R 0.8117 1.2319 0.4178 1.577
## site37R 0.8500 1.1765 0.4417 1.636
## site38R 0.7547 1.3251 0.3960 1.438
## site39R 0.8173 1.2235 0.4035 1.655
## site40R 1.1123 0.8990 0.5515 2.243
## site41R 0.8158 1.2259 0.3961 1.680
## site42R 1.0899 0.9175 0.5382 2.207
##
## Concordance= 0.59 (se = 0.009 )
## Likelihood ratio test= 111 on 42 df, p=4e-08
## Wald test = 109.4 on 42 df, p=7e-08
## Score (logrank) test = 113.7 on 42 df, p=2e-08
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ OM + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=PFF_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ OM + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = PFF_IPF,
## id = ID)
##
## n= 53867, number of events= 1131
## (849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## OM 0.0326936 1.0332339 0.0415005 0.788 0.4308
## dx_yr 0.1040354 1.1096397 0.0140529 7.403 1.33e-13 ***
## age_dx -0.0002621 0.9997380 0.0039353 -0.067 0.9469
## sexM 0.0606268 1.0625024 0.0713010 0.850 0.3952
## dich_RaceNon-White -0.0629281 0.9390110 0.1262292 -0.499 0.6181
## smokeHxEver 0.0430889 1.0440307 0.0646643 0.666 0.5052
## disadv -0.0629225 0.9390163 0.1128946 -0.557 0.5773
## site02R 0.5850645 1.7951067 0.3495436 1.674 0.0942 .
## site03R -0.2859287 0.7513162 0.3598005 -0.795 0.4268
## site04R -0.2624054 0.7691991 0.4597067 -0.571 0.5681
## site05R -0.3748213 0.6874121 0.3835164 -0.977 0.3284
## site06R -0.0758819 0.9269257 0.3416560 -0.222 0.8242
## site07R -0.3550241 0.7011566 0.3192538 -1.112 0.2661
## site08R NA NA 0.0000000 NA NA
## site09R -0.0995772 0.9052200 0.3543448 -0.281 0.7787
## site10R -0.1704998 0.8432433 0.4002159 -0.426 0.6701
## site11R -0.1574578 0.8543128 0.3230130 -0.487 0.6259
## site12R -0.1066097 0.8988764 0.3481302 -0.306 0.7594
## site13R -0.3507132 0.7041857 0.3154205 -1.112 0.2662
## site14R -0.4139160 0.6610565 0.6483655 -0.638 0.5232
## site15R -0.0249939 0.9753159 0.3471927 -0.072 0.9426
## site16R 0.0935601 1.0980766 0.3557467 0.263 0.7926
## site17R -0.0323214 0.9681953 0.3741427 -0.086 0.9312
## site18R -0.3174873 0.7279759 0.3388361 -0.937 0.3488
## site19R -0.0255824 0.9747420 0.3640588 -0.070 0.9440
## site20R -0.2954545 0.7441933 0.3608436 -0.819 0.4129
## site21R -0.3583681 0.6988158 0.3367501 -1.064 0.2872
## site22R -0.1118308 0.8941956 0.3192340 -0.350 0.7261
## site23R -0.2256642 0.7979861 0.3384212 -0.667 0.5049
## site24R -0.1763113 0.8383570 0.3448062 -0.511 0.6091
## site25R -0.0460385 0.9550052 0.3382344 -0.136 0.8917
## site26R -0.4170714 0.6589739 0.3574496 -1.167 0.2433
## site27R -0.0850962 0.9184239 0.7706431 -0.110 0.9121
## site28R -0.1662541 0.8468310 0.4464182 -0.372 0.7096
## site29R -0.3178267 0.7277289 0.4449827 -0.714 0.4751
## site30R -0.2629715 0.7687638 0.3439153 -0.765 0.4445
## site31R -0.3607265 0.6971697 0.4584399 -0.787 0.4314
## site32R -0.4062812 0.6661228 0.3903967 -1.041 0.2980
## site33R -0.3652165 0.6940464 0.3477144 -1.050 0.2936
## site34R -0.1737964 0.8404680 0.3286392 -0.529 0.5969
## site35R -0.1638864 0.8488384 0.3481140 -0.471 0.6378
## site36R -0.2491257 0.7794820 0.3541058 -0.704 0.4817
## site37R -0.2119288 0.8090223 0.3468373 -0.611 0.5412
## site38R -0.3240544 0.7232109 0.3427608 -0.945 0.3444
## site39R -0.2593504 0.7715527 0.3728587 -0.696 0.4867
## site40R 0.0571100 1.0587722 0.3720264 0.154 0.8780
## site41R -0.2480017 0.7803586 0.3791269 -0.654 0.5130
## site42R 0.0139787 1.0140769 0.3720564 0.038 0.9700
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## OM 1.0332 0.9678 0.9525 1.121
## dx_yr 1.1096 0.9012 1.0795 1.141
## age_dx 0.9997 1.0003 0.9921 1.007
## sexM 1.0625 0.9412 0.9239 1.222
## dich_RaceNon-White 0.9390 1.0650 0.7332 1.203
## smokeHxEver 1.0440 0.9578 0.9198 1.185
## disadv 0.9390 1.0649 0.7526 1.172
## site02R 1.7951 0.5571 0.9048 3.561
## site03R 0.7513 1.3310 0.3712 1.521
## site04R 0.7692 1.3001 0.3124 1.894
## site05R 0.6874 1.4547 0.3242 1.458
## site06R 0.9269 1.0788 0.4745 1.811
## site07R 0.7012 1.4262 0.3750 1.311
## site08R NA NA NA NA
## site09R 0.9052 1.1047 0.4520 1.813
## site10R 0.8432 1.1859 0.3848 1.848
## site11R 0.8543 1.1705 0.4536 1.609
## site12R 0.8989 1.1125 0.4543 1.778
## site13R 0.7042 1.4201 0.3795 1.307
## site14R 0.6611 1.5127 0.1855 2.356
## site15R 0.9753 1.0253 0.4939 1.926
## site16R 1.0981 0.9107 0.5468 2.205
## site17R 0.9682 1.0328 0.4650 2.016
## site18R 0.7280 1.3737 0.3747 1.414
## site19R 0.9747 1.0259 0.4775 1.990
## site20R 0.7442 1.3437 0.3669 1.510
## site21R 0.6988 1.4310 0.3612 1.352
## site22R 0.8942 1.1183 0.4783 1.672
## site23R 0.7980 1.2532 0.4111 1.549
## site24R 0.8384 1.1928 0.4265 1.648
## site25R 0.9550 1.0471 0.4922 1.853
## site26R 0.6590 1.5175 0.3270 1.328
## site27R 0.9184 1.0888 0.2028 4.159
## site28R 0.8468 1.1809 0.3530 2.031
## site29R 0.7277 1.3741 0.3042 1.741
## site30R 0.7688 1.3008 0.3918 1.508
## site31R 0.6972 1.4344 0.2839 1.712
## site32R 0.6661 1.5012 0.3099 1.432
## site33R 0.6940 1.4408 0.3511 1.372
## site34R 0.8405 1.1898 0.4413 1.601
## site35R 0.8488 1.1781 0.4291 1.679
## site36R 0.7795 1.2829 0.3894 1.560
## site37R 0.8090 1.2361 0.4100 1.597
## site38R 0.7232 1.3827 0.3694 1.416
## site39R 0.7716 1.2961 0.3715 1.602
## site40R 1.0588 0.9445 0.5107 2.195
## site41R 0.7804 1.2815 0.3712 1.641
## site42R 1.0141 0.9861 0.4891 2.103
##
## Concordance= 0.591 (se = 0.009 )
## Likelihood ratio test= 111.8 on 47 df, p=3e-07
## Wald test = 110.6 on 47 df, p=5e-07
## Score (logrank) test = 114.9 on 47 df, p=1e-07
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ OM + dx_yr + site, data=CARE_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ OM + dx_yr + site,
## data = CARE_IPF, id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## OM -0.06709 0.93511 0.03259 -2.058 0.03956 *
## dx_yr 0.81346 2.25570 0.03229 25.191 < 2e-16 ***
## site102 0.21991 1.24596 0.14722 1.494 0.13526
## site103 0.52353 1.68797 0.11915 4.394 1.11e-05 ***
## site104 0.38792 1.47392 0.13938 2.783 0.00538 **
## site105 0.09127 1.09556 0.12629 0.723 0.46988
## site106 0.14725 1.15865 0.11987 1.228 0.21929
## site107 0.13464 1.14413 0.19790 0.680 0.49626
## site108 -0.42351 0.65475 0.26376 -1.606 0.10835
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## OM 0.9351 1.0694 0.8772 0.9968
## dx_yr 2.2557 0.4433 2.1174 2.4031
## site102 1.2460 0.8026 0.9337 1.6627
## site103 1.6880 0.5924 1.3364 2.1320
## site104 1.4739 0.6785 1.1216 1.9369
## site105 1.0956 0.9128 0.8553 1.4033
## site106 1.1586 0.8631 0.9160 1.4655
## site107 1.1441 0.8740 0.7763 1.6863
## site108 0.6547 1.5273 0.3904 1.0980
##
## Concordance= 0.773 (se = 0.01 )
## Likelihood ratio test= 1056 on 9 df, p=<2e-16
## Wald test = 670 on 9 df, p=<2e-16
## Score (logrank) test = 587.8 on 9 df, p=<2e-16
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ OM + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=CARE_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ OM + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = CARE_IPF,
## id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## OM -0.075853 0.926953 0.033281 -2.279 0.02266 *
## dx_yr 0.813131 2.254956 0.032448 25.059 < 2e-16 ***
## age_dx 0.012625 1.012705 0.004511 2.799 0.00513 **
## sexF -0.040532 0.960278 0.077048 -0.526 0.59884
## dich_RaceNon-White -0.141492 0.868062 0.110343 -1.282 0.19974
## smokeHxFormer -0.002835 0.997169 0.081474 -0.035 0.97224
## smokeHxAlways 0.049157 1.050385 0.168933 0.291 0.77106
## smokeHxUnknown 0.437822 1.549328 0.734808 0.596 0.55129
## disadv 0.021065 1.021288 0.130396 0.162 0.87166
## site102 0.206241 1.229050 0.150076 1.374 0.16937
## site103 0.538568 1.713550 0.121814 4.421 9.81e-06 ***
## site104 0.397174 1.487614 0.139924 2.838 0.00453 **
## site105 0.113843 1.120576 0.129412 0.880 0.37903
## site106 0.119709 1.127169 0.121016 0.989 0.32256
## site107 0.072980 1.075709 0.204978 0.356 0.72181
## site108 -0.426751 0.652626 0.267118 -1.598 0.11013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## OM 0.9270 1.0788 0.8684 0.9894
## dx_yr 2.2550 0.4435 2.1160 2.4030
## age_dx 1.0127 0.9875 1.0038 1.0217
## sexF 0.9603 1.0414 0.8257 1.1168
## dich_RaceNon-White 0.8681 1.1520 0.6992 1.0776
## smokeHxFormer 0.9972 1.0028 0.8500 1.1698
## smokeHxAlways 1.0504 0.9520 0.7543 1.4627
## smokeHxUnknown 1.5493 0.6454 0.3670 6.5405
## disadv 1.0213 0.9792 0.7910 1.3187
## site102 1.2290 0.8136 0.9158 1.6494
## site103 1.7136 0.5836 1.3496 2.1756
## site104 1.4876 0.6722 1.1308 1.9570
## site105 1.1206 0.8924 0.8695 1.4441
## site106 1.1272 0.8872 0.8892 1.4289
## site107 1.0757 0.9296 0.7198 1.6076
## site108 0.6526 1.5323 0.3866 1.1016
##
## Concordance= 0.775 (se = 0.01 )
## Likelihood ratio test= 1069 on 16 df, p=<2e-16
## Wald test = 677.9 on 16 df, p=<2e-16
## Score (logrank) test = 594.1 on 16 df, p=<2e-16
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ OM + dx_yr + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ OM + dx_yr + site,
## data = All_IPF, id = ID, cluster = cohort)
##
## n= 123367, number of events= 2749
## (723 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## OM 0.008346 1.008381 0.021735 0.047598 0.175 0.860813
## dx_yr 0.104256 1.109884 0.006955 0.074653 1.397 0.162550
## site02R 0.664066 1.942675 0.334586 0.051497 12.895 < 2e-16 ***
## site03R -0.275403 0.759266 0.347078 0.019242 -14.313 < 2e-16 ***
## site04R -0.224335 0.799047 0.449438 0.091090 -2.463 0.013786 *
## site05R -0.335114 0.715257 0.369060 0.036067 -9.291 < 2e-16 ***
## site06R -0.049078 0.952106 0.325096 0.094334 -0.520 0.602880
## site07R -0.324164 0.723131 0.304795 0.094544 -3.429 0.000606 ***
## site09R -0.035434 0.965186 0.339723 0.017681 -2.004 0.045061 *
## site1 0.242521 1.274458 0.281363 0.119281 2.033 0.042034 *
## site101 -0.109665 0.896134 0.293302 0.136328 -0.804 0.421154
## site102 -0.236867 0.789096 0.297880 0.110874 -2.136 0.032650 *
## site103 0.097367 1.102265 0.286293 0.088352 1.102 0.270443
## site104 -0.190366 0.826656 0.294726 0.079796 -2.386 0.017049 *
## site105 0.080056 1.083348 0.289903 0.120477 0.664 0.506375
## site106 -0.212939 0.808206 0.286537 0.073151 -2.911 0.003604 **
## site107 0.211391 1.235396 0.326950 0.138460 1.527 0.126828
## site108 0.152312 1.164524 0.369607 0.222935 0.683 0.494473
## site10R -0.141991 0.867629 0.385921 0.120967 -1.174 0.240478
## site11R -0.069778 0.932601 0.300606 0.006433 -10.848 < 2e-16 ***
## site12R -0.095622 0.908807 0.334148 0.021122 -4.527 5.98e-06 ***
## site13R -0.298481 0.741945 0.301811 0.094185 -3.169 0.001529 **
## site14R -0.365042 0.694167 0.640720 0.111768 -3.266 0.001091 **
## site15R 0.030659 1.031134 0.330657 0.014133 2.169 0.030060 *
## site16R 0.140725 1.151108 0.344387 0.031560 4.459 8.24e-06 ***
## site17R 0.038494 1.039245 0.360084 0.014346 2.683 0.007292 **
## site18R -0.264889 0.767291 0.321827 0.133761 -1.980 0.047668 *
## site19R -0.022981 0.977281 0.350635 0.057136 -0.402 0.687526
## site20R -0.272055 0.761813 0.347097 0.023017 -11.820 < 2e-16 ***
## site21R -0.320116 0.726065 0.320161 0.015096 -21.205 < 2e-16 ***
## site22R -0.070026 0.932369 0.305090 0.012464 -5.618 1.93e-08 ***
## site23R -0.165905 0.847126 0.322482 0.008582 -19.333 < 2e-16 ***
## site24R -0.160898 0.851379 0.330845 0.027024 -5.954 2.62e-09 ***
## site25R -0.023385 0.976886 0.324883 0.041771 -0.560 0.575587
## site26R -0.410523 0.663303 0.344560 0.099356 -4.132 3.60e-05 ***
## site27R -0.014297 0.985805 0.759679 0.120008 -0.119 0.905169
## site28R -0.146860 0.863415 0.433952 0.037145 -3.954 7.69e-05 ***
## site29R -0.301680 0.739574 0.433826 0.069702 -4.328 1.50e-05 ***
## site30R -0.213175 0.808014 0.330584 0.093118 -2.289 0.022062 *
## site31R -0.334912 0.715401 0.449440 0.075244 -4.451 8.55e-06 ***
## site32R -0.352451 0.702963 0.368485 0.037966 -9.283 < 2e-16 ***
## site33R -0.350559 0.704294 0.332178 0.095597 -3.667 0.000245 ***
## site34R -0.157525 0.854256 0.313516 0.059478 -2.648 0.008086 **
## site35R -0.114006 0.892253 0.333822 0.046095 -2.473 0.013387 *
## site36R -0.226130 0.797615 0.338070 0.032639 -6.928 4.26e-12 ***
## site37R -0.185355 0.830809 0.333883 0.038438 -4.822 1.42e-06 ***
## site38R -0.298606 0.741852 0.328981 0.024737 -12.071 < 2e-16 ***
## site39R -0.220709 0.801950 0.360016 0.013721 -16.085 < 2e-16 ***
## site40R 0.148627 1.160240 0.346586 0.017525 8.481 < 2e-16 ***
## site41R -0.214543 0.806910 0.368524 0.028918 -7.419 1.18e-13 ***
## site42R 0.082961 1.086499 0.359962 0.023821 3.483 0.000496 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## OM 1.0084 0.9917 0.9186 1.1070
## dx_yr 1.1099 0.9010 0.9588 1.2848
## site02R 1.9427 0.5148 1.7562 2.1490
## site03R 0.7593 1.3171 0.7312 0.7884
## site04R 0.7990 1.2515 0.6684 0.9552
## site05R 0.7153 1.3981 0.6664 0.7676
## site06R 0.9521 1.0503 0.7914 1.1455
## site07R 0.7231 1.3829 0.6008 0.8703
## site09R 0.9652 1.0361 0.9323 0.9992
## site1 1.2745 0.7846 1.0088 1.6101
## site101 0.8961 1.1159 0.6860 1.1706
## site102 0.7891 1.2673 0.6350 0.9806
## site103 1.1023 0.9072 0.9270 1.3107
## site104 0.8267 1.2097 0.7070 0.9666
## site105 1.0833 0.9231 0.8555 1.3719
## site106 0.8082 1.2373 0.7003 0.9328
## site107 1.2354 0.8095 0.9418 1.6206
## site108 1.1645 0.8587 0.7523 1.8026
## site10R 0.8676 1.1526 0.6845 1.0998
## site11R 0.9326 1.0723 0.9209 0.9444
## site12R 0.9088 1.1003 0.8720 0.9472
## site13R 0.7419 1.3478 0.6169 0.8924
## site14R 0.6942 1.4406 0.5576 0.8642
## site15R 1.0311 0.9698 1.0030 1.0601
## site16R 1.1511 0.8687 1.0821 1.2246
## site17R 1.0392 0.9622 1.0104 1.0689
## site18R 0.7673 1.3033 0.5903 0.9973
## site19R 0.9773 1.0232 0.8737 1.0931
## site20R 0.7618 1.3127 0.7282 0.7970
## site21R 0.7261 1.3773 0.7049 0.7479
## site22R 0.9324 1.0725 0.9099 0.9554
## site23R 0.8471 1.1805 0.8330 0.8615
## site24R 0.8514 1.1746 0.8075 0.8977
## site25R 0.9769 1.0237 0.9001 1.0602
## site26R 0.6633 1.5076 0.5459 0.8059
## site27R 0.9858 1.0144 0.7792 1.2472
## site28R 0.8634 1.1582 0.8028 0.9286
## site29R 0.7396 1.3521 0.6451 0.8478
## site30R 0.8080 1.2376 0.6732 0.9698
## site31R 0.7154 1.3978 0.6173 0.8291
## site32R 0.7030 1.4226 0.6526 0.7573
## site33R 0.7043 1.4199 0.5840 0.8494
## site34R 0.8543 1.1706 0.7603 0.9599
## site35R 0.8923 1.1208 0.8152 0.9766
## site36R 0.7976 1.2537 0.7482 0.8503
## site37R 0.8308 1.2036 0.7705 0.8958
## site38R 0.7419 1.3480 0.7067 0.7787
## site39R 0.8019 1.2470 0.7807 0.8238
## site40R 1.1602 0.8619 1.1211 1.2008
## site41R 0.8069 1.2393 0.7624 0.8540
## site42R 1.0865 0.9204 1.0369 1.1384
##
## Concordance= 0.595 (se = 0.043 )
## Likelihood ratio test= 347.9 on 51 df, p=<2e-16
## Wald test = 148.6 on 51 df, p=2e-11
## Score (logrank) test = 337.6 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ OM + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ OM + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = All_IPF,
## id = ID, cluster = cohort)
##
## n= 121241, number of events= 2709
## (2849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## OM 0.004681 1.004692 0.022151 0.041890 0.112 0.911025
## dx_yr 0.100974 1.106248 0.007326 0.079954 1.263 0.206620
## age_dx 0.004216 1.004225 0.002447 0.002208 1.910 0.056185 .
## sexF -0.158003 0.853847 0.044865 0.084182 -1.877 0.060527 .
## dich_RaceNon-White 0.017006 1.017151 0.068701 0.058620 0.290 0.771734
## smokeHxFormer 0.099364 1.104468 0.059381 0.041267 2.408 0.016048 *
## smokeHxAlways -0.031110 0.969369 0.138358 0.117532 -0.265 0.791247
## smokeHxUnknown 0.316941 1.372921 0.153843 0.222183 1.426 0.153728
## smokeHxEver 0.036076 1.036735 0.064257 0.008292 4.351 1.36e-05 ***
## disadv 0.041195 1.042056 0.070848 0.117076 0.352 0.724936
## site02R 0.652366 1.920078 0.345392 0.067782 9.625 < 2e-16 ***
## site03R -0.253805 0.775843 0.357290 0.017898 -14.180 < 2e-16 ***
## site04R -0.219520 0.802904 0.457637 0.102680 -2.138 0.032525 *
## site05R -0.328336 0.720121 0.379714 0.034133 -9.619 < 2e-16 ***
## site06R -0.059713 0.942035 0.337836 0.121345 -0.492 0.622655
## site07R -0.331678 0.717718 0.316470 0.088483 -3.748 0.000178 ***
## site09R -0.083470 0.919919 0.352062 0.001438 -58.031 < 2e-16 ***
## site1 0.175666 1.192040 0.297702 0.164465 1.068 0.285473
## site101 -0.161604 0.850778 0.308371 0.115385 -1.401 0.161344
## site102 -0.307594 0.735214 0.313627 0.096765 -3.179 0.001479 **
## site103 0.020282 1.020489 0.301990 0.045563 0.445 0.656222
## site104 -0.255829 0.774275 0.310609 0.045097 -5.673 1.40e-08 ***
## site105 0.022701 1.022961 0.307198 0.101970 0.223 0.823825
## site106 -0.290768 0.747689 0.302663 0.049020 -5.932 3.00e-09 ***
## site107 0.118892 1.126248 0.342631 0.110474 1.076 0.281837
## site108 0.075676 1.078613 0.381971 0.192640 0.393 0.694439
## site10R -0.211530 0.809345 0.396500 0.087528 -2.417 0.015661 *
## site11R -0.099368 0.905409 0.315281 0.018643 -5.330 9.82e-08 ***
## site12R -0.118999 0.887809 0.345131 0.011043 -10.776 < 2e-16 ***
## site13R -0.336484 0.714278 0.313407 0.088850 -3.787 0.000152 ***
## site14R -0.372734 0.688848 0.646409 0.094223 -3.956 7.63e-05 ***
## site15R -0.008039 0.991994 0.342920 0.036392 -0.221 0.825181
## site16R 0.143184 1.153943 0.354452 0.047487 3.015 0.002568 **
## site17R 0.025963 1.026303 0.370946 0.027066 0.959 0.337428
## site18R -0.294331 0.745030 0.333908 0.108701 -2.708 0.006775 **
## site19R -0.069435 0.932921 0.361039 0.036169 -1.920 0.054889 .
## site20R -0.269864 0.763483 0.358311 0.012276 -21.982 < 2e-16 ***
## site21R -0.354576 0.701471 0.331817 0.019419 -18.259 < 2e-16 ***
## site22R -0.081510 0.921723 0.316971 0.013788 -5.912 3.38e-09 ***
## site23R -0.223322 0.799857 0.336226 0.022857 -9.770 < 2e-16 ***
## site24R -0.175609 0.838946 0.342274 0.030820 -5.698 1.21e-08 ***
## site25R -0.039470 0.961299 0.336777 0.052945 -0.745 0.455975
## site26R -0.451676 0.636560 0.355351 0.067788 -6.663 2.68e-11 ***
## site27R 0.000429 1.000429 0.766948 0.123980 0.003 0.997239
## site28R -0.144701 0.865281 0.442781 0.042004 -3.445 0.000571 ***
## site29R -0.308749 0.734365 0.442857 0.091163 -3.387 0.000707 ***
## site30R -0.276432 0.758485 0.342778 0.080637 -3.428 0.000608 ***
## site31R -0.371206 0.689902 0.457592 0.059850 -6.202 5.57e-10 ***
## site32R -0.395238 0.673520 0.388759 0.020311 -19.460 < 2e-16 ***
## site33R -0.391568 0.675996 0.345261 0.066884 -5.854 4.79e-09 ***
## site34R -0.173880 0.840397 0.326085 0.051279 -3.391 0.000697 ***
## site35R -0.166380 0.846724 0.346841 0.025383 -6.555 5.57e-11 ***
## site36R -0.229358 0.795044 0.349409 0.035515 -6.458 1.06e-10 ***
## site37R -0.201270 0.817692 0.344772 0.038922 -5.171 2.33e-07 ***
## site38R -0.304846 0.737237 0.340284 0.003326 -91.652 < 2e-16 ***
## site39R -0.262556 0.769083 0.370552 0.016135 -16.272 < 2e-16 ***
## site40R 0.114435 1.121240 0.357337 0.031877 3.590 0.000331 ***
## site41R -0.253223 0.776295 0.377847 0.016139 -15.690 < 2e-16 ***
## site42R 0.057802 1.059505 0.370059 0.027661 2.090 0.036650 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## OM 1.0047 0.9953 0.9255 1.0907
## dx_yr 1.1062 0.9040 0.9458 1.2939
## age_dx 1.0042 0.9958 0.9999 1.0086
## sexF 0.8538 1.1712 0.7240 1.0070
## dich_RaceNon-White 1.0172 0.9831 0.9068 1.1410
## smokeHxFormer 1.1045 0.9054 1.0187 1.1975
## smokeHxAlways 0.9694 1.0316 0.7699 1.2205
## smokeHxUnknown 1.3729 0.7284 0.8882 2.1221
## smokeHxEver 1.0367 0.9646 1.0200 1.0537
## disadv 1.0421 0.9596 0.8284 1.3108
## site02R 1.9201 0.5208 1.6812 2.1929
## site03R 0.7758 1.2889 0.7491 0.8035
## site04R 0.8029 1.2455 0.6565 0.9819
## site05R 0.7201 1.3887 0.6735 0.7699
## site06R 0.9420 1.0615 0.7426 1.1950
## site07R 0.7177 1.3933 0.6034 0.8536
## site09R 0.9199 1.0871 0.9173 0.9225
## site1 1.1920 0.8389 0.8636 1.6454
## site101 0.8508 1.1754 0.6786 1.0667
## site102 0.7352 1.3601 0.6082 0.8888
## site103 1.0205 0.9799 0.9333 1.1158
## site104 0.7743 1.2915 0.7088 0.8458
## site105 1.0230 0.9776 0.8376 1.2493
## site106 0.7477 1.3375 0.6792 0.8231
## site107 1.1262 0.8879 0.9070 1.3985
## site108 1.0786 0.9271 0.7394 1.5734
## site10R 0.8093 1.2356 0.6818 0.9608
## site11R 0.9054 1.1045 0.8729 0.9391
## site12R 0.8878 1.1264 0.8688 0.9072
## site13R 0.7143 1.4000 0.6001 0.8502
## site14R 0.6888 1.4517 0.5727 0.8286
## site15R 0.9920 1.0081 0.9237 1.0653
## site16R 1.1539 0.8666 1.0514 1.2665
## site17R 1.0263 0.9744 0.9733 1.0822
## site18R 0.7450 1.3422 0.6021 0.9219
## site19R 0.9329 1.0719 0.8691 1.0015
## site20R 0.7635 1.3098 0.7453 0.7821
## site21R 0.7015 1.4256 0.6753 0.7287
## site22R 0.9217 1.0849 0.8971 0.9470
## site23R 0.7999 1.2502 0.7648 0.8365
## site24R 0.8389 1.1920 0.7898 0.8912
## site25R 0.9613 1.0403 0.8665 1.0664
## site26R 0.6366 1.5709 0.5574 0.7270
## site27R 1.0004 0.9996 0.7846 1.2756
## site28R 0.8653 1.1557 0.7969 0.9395
## site29R 0.7344 1.3617 0.6142 0.8780
## site30R 0.7585 1.3184 0.6476 0.8884
## site31R 0.6899 1.4495 0.6135 0.7758
## site32R 0.6735 1.4847 0.6472 0.7009
## site33R 0.6760 1.4793 0.5929 0.7707
## site34R 0.8404 1.1899 0.7600 0.9293
## site35R 0.8467 1.1810 0.8056 0.8899
## site36R 0.7950 1.2578 0.7416 0.8524
## site37R 0.8177 1.2230 0.7576 0.8825
## site38R 0.7372 1.3564 0.7324 0.7421
## site39R 0.7691 1.3002 0.7451 0.7938
## site40R 1.1212 0.8919 1.0533 1.1935
## site41R 0.7763 1.2882 0.7521 0.8012
## site42R 1.0595 0.9438 1.0036 1.1185
##
## Concordance= 0.598 (se = 0.039 )
## Likelihood ratio test= 379.9 on 59 df, p=<2e-16
## Wald test = 35.12 on 59 df, p=1
## Score (logrank) test = 374.4 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ SS + dx_yr, data=Simm_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SS + dx_yr, data = Simm_IPF,
## id = ID)
##
## n= 31982, number of events= 695
## (446 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SS 0.47439 1.60703 0.27965 1.696 0.0898 .
## dx_yr 0.02377 1.02406 0.00845 2.813 0.0049 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SS 1.607 0.6223 0.9289 2.780
## dx_yr 1.024 0.9765 1.0072 1.041
##
## Concordance= 0.524 (se = 0.012 )
## Likelihood ratio test= 10.26 on 2 df, p=0.006
## Wald test = 10.68 on 2 df, p=0.005
## Score (logrank) test = 10.71 on 2 df, p=0.005
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ SS + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv, data=Simm_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SS + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv, data = Simm_IPF, id = ID)
##
## n= 30488, number of events= 670
## (1940 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SS 0.368463 1.445511 0.300869 1.225 0.22070
## dx_yr 0.011621 1.011689 0.009072 1.281 0.20020
## age_dx 0.002711 1.002715 0.004576 0.593 0.55346
## sexF -0.372239 0.689189 0.089029 -4.181 2.9e-05 ***
## dich_RaceNon-White 0.155189 1.167878 0.128361 1.209 0.22666
## smokeHxFormer 0.084484 1.088156 0.091868 0.920 0.35777
## smokeHxAlways -0.408091 0.664918 0.266268 -1.533 0.12537
## smokeHxUnknown 0.495109 1.640676 0.170364 2.906 0.00366 **
## disadv 0.382720 1.466267 0.133294 2.871 0.00409 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SS 1.4455 0.6918 0.8015 2.6069
## dx_yr 1.0117 0.9884 0.9939 1.0298
## age_dx 1.0027 0.9973 0.9938 1.0117
## sexF 0.6892 1.4510 0.5788 0.8206
## dich_RaceNon-White 1.1679 0.8563 0.9081 1.5020
## smokeHxFormer 1.0882 0.9190 0.9089 1.3028
## smokeHxAlways 0.6649 1.5039 0.3946 1.1205
## smokeHxUnknown 1.6407 0.6095 1.1749 2.2911
## disadv 1.4663 0.6820 1.1292 1.9040
##
## Concordance= 0.592 (se = 0.012 )
## Likelihood ratio test= 53.04 on 9 df, p=3e-08
## Wald test = 54.09 on 9 df, p=2e-08
## Score (logrank) test = 54.86 on 9 df, p=1e-08
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ SS + dx_yr + site, data=PFF_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SS + dx_yr + site,
## data = PFF_IPF, id = ID)
##
## n= 54499, number of events= 1146
## (217 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SS 0.012132 1.012206 0.146170 0.083 0.934
## dx_yr 0.104169 1.109788 0.013561 7.682 1.57e-14 ***
## site02R 0.643993 1.904068 0.381077 1.690 0.091 .
## site03R -0.243484 0.783892 0.347691 -0.700 0.484
## site04R -0.205944 0.813879 0.449637 -0.458 0.647
## site05R -0.296800 0.743193 0.369167 -0.804 0.421
## site06R -0.020232 0.979971 0.325778 -0.062 0.950
## site07R -0.301975 0.739356 0.305022 -0.990 0.322
## site08R NA NA 0.000000 NA NA
## site09R -0.013630 0.986463 0.340068 -0.040 0.968
## site10R -0.155023 0.856395 0.402947 -0.385 0.700
## site11R -0.056393 0.945168 0.299531 -0.188 0.851
## site12R -0.077962 0.924999 0.334777 -0.233 0.816
## site13R -0.284715 0.752229 0.302073 -0.943 0.346
## site14R -0.349780 0.704843 0.641269 -0.545 0.585
## site15R 0.041796 1.042682 0.331362 0.126 0.900
## site16R 0.141405 1.151891 0.345124 0.410 0.682
## site17R 0.044760 1.045777 0.360440 0.124 0.901
## site18R -0.266729 0.765881 0.323816 -0.824 0.410
## site19R -0.008356 0.991679 0.350097 -0.024 0.981
## site20R -0.240895 0.785924 0.347874 -0.692 0.489
## site21R -0.278103 0.757219 0.319515 -0.870 0.384
## site22R -0.054596 0.946867 0.305497 -0.179 0.858
## site23R -0.151175 0.859697 0.322467 -0.469 0.639
## site24R -0.132746 0.875688 0.331557 -0.400 0.689
## site25R -0.017925 0.982235 0.363597 -0.049 0.961
## site26R -0.400218 0.670174 0.354289 -1.130 0.259
## site27R 0.023814 1.024100 0.759872 0.031 0.975
## site28R -0.108669 0.897027 0.434013 -0.250 0.802
## site29R -0.277885 0.757384 0.436755 -0.636 0.525
## site30R -0.197830 0.820509 0.330810 -0.598 0.550
## site31R -0.307811 0.735054 0.449879 -0.684 0.494
## site32R -0.341448 0.710740 0.368528 -0.927 0.354
## site33R -0.332222 0.717328 0.334975 -0.992 0.321
## site34R -0.136132 0.872727 0.313602 -0.434 0.664
## site35R -0.104672 0.900620 0.334014 -0.313 0.754
## site36R -0.188279 0.828384 0.337914 -0.557 0.577
## site37R -0.157661 0.854139 0.333958 -0.472 0.637
## site38R -0.276427 0.758489 0.328990 -0.840 0.401
## site39R -0.196625 0.821499 0.360829 -0.545 0.586
## site40R 0.170358 1.185730 0.346234 0.492 0.623
## site41R -0.199369 0.819248 0.369200 -0.540 0.589
## site42R 0.086184 1.090007 0.360004 0.239 0.811
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SS 1.0122 0.9879 0.7601 1.348
## dx_yr 1.1098 0.9011 1.0807 1.140
## site02R 1.9041 0.5252 0.9022 4.018
## site03R 0.7839 1.2757 0.3966 1.550
## site04R 0.8139 1.2287 0.3372 1.965
## site05R 0.7432 1.3455 0.3605 1.532
## site06R 0.9800 1.0204 0.5175 1.856
## site07R 0.7394 1.3525 0.4066 1.344
## site08R NA NA NA NA
## site09R 0.9865 1.0137 0.5065 1.921
## site10R 0.8564 1.1677 0.3888 1.887
## site11R 0.9452 1.0580 0.5255 1.700
## site12R 0.9250 1.0811 0.4799 1.783
## site13R 0.7522 1.3294 0.4161 1.360
## site14R 0.7048 1.4188 0.2006 2.477
## site15R 1.0427 0.9591 0.5446 1.996
## site16R 1.1519 0.8681 0.5857 2.266
## site17R 1.0458 0.9562 0.5160 2.120
## site18R 0.7659 1.3057 0.4060 1.445
## site19R 0.9917 1.0084 0.4993 1.970
## site20R 0.7859 1.2724 0.3974 1.554
## site21R 0.7572 1.3206 0.4048 1.416
## site22R 0.9469 1.0561 0.5203 1.723
## site23R 0.8597 1.1632 0.4569 1.617
## site24R 0.8757 1.1420 0.4572 1.677
## site25R 0.9822 1.0181 0.4816 2.003
## site26R 0.6702 1.4921 0.3347 1.342
## site27R 1.0241 0.9765 0.2310 4.541
## site28R 0.8970 1.1148 0.3832 2.100
## site29R 0.7574 1.3203 0.3218 1.783
## site30R 0.8205 1.2188 0.4290 1.569
## site31R 0.7351 1.3604 0.3044 1.775
## site32R 0.7107 1.4070 0.3452 1.464
## site33R 0.7173 1.3941 0.3720 1.383
## site34R 0.8727 1.1458 0.4720 1.614
## site35R 0.9006 1.1103 0.4680 1.733
## site36R 0.8284 1.2072 0.4272 1.606
## site37R 0.8541 1.1708 0.4439 1.644
## site38R 0.7585 1.3184 0.3980 1.445
## site39R 0.8215 1.2173 0.4050 1.666
## site40R 1.1857 0.8434 0.6015 2.337
## site41R 0.8192 1.2206 0.3973 1.689
## site42R 1.0900 0.9174 0.5383 2.207
##
## Concordance= 0.59 (se = 0.009 )
## Likelihood ratio test= 110.5 on 42 df, p=4e-08
## Wald test = 108.8 on 42 df, p=8e-08
## Score (logrank) test = 113.1 on 42 df, p=2e-08
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ SS + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=PFF_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SS + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = PFF_IPF,
## id = ID)
##
## n= 53867, number of events= 1131
## (849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SS 0.0218929 1.0221343 0.1491498 0.147 0.883
## dx_yr 0.1027901 1.1082588 0.0139995 7.342 2.1e-13 ***
## age_dx -0.0002854 0.9997146 0.0039360 -0.073 0.942
## sexM 0.0622603 1.0642394 0.0713709 0.872 0.383
## dich_RaceNon-White -0.0590671 0.9426435 0.1262727 -0.468 0.640
## smokeHxEver 0.0433413 1.0442943 0.0646418 0.670 0.503
## disadv -0.0514455 0.9498554 0.1122989 -0.458 0.647
## site02R 0.5864974 1.7976808 0.3948631 1.485 0.137
## site03R -0.2677114 0.7651286 0.3594977 -0.745 0.456
## site04R -0.2550642 0.7748667 0.4595574 -0.555 0.579
## site05R -0.3351150 0.7152558 0.3808214 -0.880 0.379
## site06R -0.0512778 0.9500147 0.3406824 -0.151 0.880
## site07R -0.3400899 0.7117064 0.3186097 -1.067 0.286
## site08R NA NA 0.0000000 NA NA
## site09R -0.0909110 0.9130990 0.3544148 -0.257 0.798
## site10R -0.2164570 0.8053672 0.4178697 -0.518 0.604
## site11R -0.1087201 0.8969815 0.3169932 -0.343 0.732
## site12R -0.1167564 0.8898019 0.3480799 -0.335 0.737
## site13R -0.3370267 0.7138898 0.3148859 -1.070 0.284
## site14R -0.4067233 0.6658284 0.6482548 -0.627 0.530
## site15R -0.0040338 0.9959743 0.3470109 -0.012 0.991
## site16R 0.0993052 1.1044033 0.3564090 0.279 0.781
## site17R -0.0064610 0.9935599 0.3730889 -0.017 0.986
## site18R -0.2986036 0.7418534 0.3393004 -0.880 0.379
## site19R -0.0508260 0.9504440 0.3624229 -0.140 0.888
## site20R -0.2868189 0.7506476 0.3610845 -0.794 0.427
## site21R -0.3162291 0.7288925 0.3326060 -0.951 0.342
## site22R -0.1041534 0.9010871 0.3193933 -0.326 0.744
## site23R -0.2106743 0.8100379 0.3377794 -0.624 0.533
## site24R -0.1831265 0.8326629 0.3447836 -0.531 0.595
## site25R -0.0558576 0.9456738 0.3753215 -0.149 0.882
## site26R -0.4353478 0.6470396 0.3680660 -1.183 0.237
## site27R -0.0767666 0.9261059 0.7705123 -0.100 0.921
## site28R -0.1339878 0.8746007 0.4447679 -0.301 0.763
## site29R -0.3304603 0.7185929 0.4481229 -0.737 0.461
## site30R -0.2477709 0.7805388 0.3433925 -0.722 0.471
## site31R -0.3529850 0.7025878 0.4586174 -0.770 0.441
## site32R -0.3959510 0.6730397 0.3901100 -1.015 0.310
## site33R -0.3721915 0.6892222 0.3509372 -1.061 0.289
## site34R -0.1744177 0.8399460 0.3285173 -0.531 0.595
## site35R -0.1617393 0.8506629 0.3483175 -0.464 0.642
## site36R -0.2150827 0.8064747 0.3516336 -0.612 0.541
## site37R -0.1961081 0.8219234 0.3462448 -0.566 0.571
## site38R -0.3096865 0.7336769 0.3422130 -0.905 0.365
## site39R -0.2423828 0.7847557 0.3727553 -0.650 0.516
## site40R 0.1402910 1.1506086 0.3571384 0.393 0.694
## site41R -0.2351186 0.7904771 0.3794477 -0.620 0.535
## site42R 0.0249730 1.0252875 0.3716900 0.067 0.946
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SS 1.0221 0.9783 0.7630 1.369
## dx_yr 1.1083 0.9023 1.0783 1.139
## age_dx 0.9997 1.0003 0.9920 1.007
## sexM 1.0642 0.9396 0.9253 1.224
## dich_RaceNon-White 0.9426 1.0608 0.7360 1.207
## smokeHxEver 1.0443 0.9576 0.9200 1.185
## disadv 0.9499 1.0528 0.7622 1.184
## site02R 1.7977 0.5563 0.8291 3.898
## site03R 0.7651 1.3070 0.3782 1.548
## site04R 0.7749 1.2905 0.3148 1.907
## site05R 0.7153 1.3981 0.3391 1.509
## site06R 0.9500 1.0526 0.4872 1.852
## site07R 0.7117 1.4051 0.3812 1.329
## site08R NA NA NA NA
## site09R 0.9131 1.0952 0.4559 1.829
## site10R 0.8054 1.2417 0.3551 1.827
## site11R 0.8970 1.1149 0.4819 1.670
## site12R 0.8898 1.1238 0.4498 1.760
## site13R 0.7139 1.4008 0.3851 1.323
## site14R 0.6658 1.5019 0.1869 2.372
## site15R 0.9960 1.0040 0.5045 1.966
## site16R 1.1044 0.9055 0.5492 2.221
## site17R 0.9936 1.0065 0.4782 2.064
## site18R 0.7419 1.3480 0.3815 1.443
## site19R 0.9504 1.0521 0.4671 1.934
## site20R 0.7506 1.3322 0.3699 1.523
## site21R 0.7289 1.3719 0.3798 1.399
## site22R 0.9011 1.1098 0.4818 1.685
## site23R 0.8100 1.2345 0.4178 1.570
## site24R 0.8327 1.2010 0.4236 1.637
## site25R 0.9457 1.0574 0.4532 1.973
## site26R 0.6470 1.5455 0.3145 1.331
## site27R 0.9261 1.0798 0.2045 4.193
## site28R 0.8746 1.1434 0.3658 2.091
## site29R 0.7186 1.3916 0.2986 1.730
## site30R 0.7805 1.2812 0.3982 1.530
## site31R 0.7026 1.4233 0.2860 1.726
## site32R 0.6730 1.4858 0.3133 1.446
## site33R 0.6892 1.4509 0.3465 1.371
## site34R 0.8399 1.1906 0.4412 1.599
## site35R 0.8507 1.1756 0.4298 1.684
## site36R 0.8065 1.2400 0.4048 1.607
## site37R 0.8219 1.2167 0.4170 1.620
## site38R 0.7337 1.3630 0.3752 1.435
## site39R 0.7848 1.2743 0.3780 1.629
## site40R 1.1506 0.8691 0.5714 2.317
## site41R 0.7905 1.2651 0.3758 1.663
## site42R 1.0253 0.9753 0.4948 2.124
##
## Concordance= 0.591 (se = 0.009 )
## Likelihood ratio test= 111.2 on 47 df, p=4e-07
## Wald test = 109.8 on 47 df, p=6e-07
## Score (logrank) test = 114.2 on 47 df, p=2e-07
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ SS + dx_yr + site, data=CARE_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SS + dx_yr + site,
## data = CARE_IPF, id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SS -0.12109 0.88596 0.49795 -0.243 0.80787
## dx_yr 0.81424 2.25746 0.03235 25.169 < 2e-16 ***
## site102 0.19860 1.21970 0.17179 1.156 0.24764
## site103 0.52716 1.69412 0.12162 4.334 1.46e-05 ***
## site104 0.37377 1.45320 0.13921 2.685 0.00725 **
## site105 0.06562 1.06782 0.12613 0.520 0.60289
## site106 0.12745 1.13593 0.12023 1.060 0.28912
## site107 0.10624 1.11209 0.19747 0.538 0.59058
## site108 -0.41809 0.65831 0.27812 -1.503 0.13278
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SS 0.8860 1.1287 0.3339 2.351
## dx_yr 2.2575 0.4430 2.1188 2.405
## site102 1.2197 0.8199 0.8710 1.708
## site103 1.6941 0.5903 1.3348 2.150
## site104 1.4532 0.6881 1.1062 1.909
## site105 1.0678 0.9365 0.8339 1.367
## site106 1.1359 0.8803 0.8974 1.438
## site107 1.1121 0.8992 0.7552 1.638
## site108 0.6583 1.5191 0.3817 1.135
##
## Concordance= 0.774 (se = 0.01 )
## Likelihood ratio test= 1052 on 9 df, p=<2e-16
## Wald test = 665.9 on 9 df, p=<2e-16
## Score (logrank) test = 588.3 on 9 df, p=<2e-16
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ SS + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=CARE_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SS + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = CARE_IPF,
## id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## SS -0.156608 0.855039 0.507237 -0.309 0.75751
## dx_yr 0.813552 2.255907 0.032493 25.038 < 2e-16 ***
## age_dx 0.012007 1.012079 0.004498 2.669 0.00760 **
## sexF -0.046391 0.954669 0.077129 -0.601 0.54753
## dich_RaceNon-White -0.139844 0.869494 0.110260 -1.268 0.20469
## smokeHxFormer 0.008827 1.008866 0.081359 0.108 0.91360
## smokeHxAlways 0.053362 1.054811 0.168742 0.316 0.75183
## smokeHxUnknown 0.401423 1.493949 0.734621 0.546 0.58477
## disadv -0.018446 0.981723 0.130105 -0.142 0.88725
## site102 0.171078 1.186583 0.173472 0.986 0.32403
## site103 0.545463 1.725407 0.123730 4.408 1.04e-05 ***
## site104 0.383722 1.467737 0.139757 2.746 0.00604 **
## site105 0.084237 1.087887 0.129388 0.651 0.51502
## site106 0.095661 1.100386 0.121294 0.789 0.43030
## site107 0.047112 1.048240 0.204823 0.230 0.81808
## site108 -0.420855 0.656485 0.282620 -1.489 0.13646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SS 0.8550 1.1695 0.3164 2.311
## dx_yr 2.2559 0.4433 2.1167 2.404
## age_dx 1.0121 0.9881 1.0032 1.021
## sexF 0.9547 1.0475 0.8207 1.110
## dich_RaceNon-White 0.8695 1.1501 0.7005 1.079
## smokeHxFormer 1.0089 0.9912 0.8602 1.183
## smokeHxAlways 1.0548 0.9480 0.7578 1.468
## smokeHxUnknown 1.4939 0.6694 0.3540 6.304
## disadv 0.9817 1.0186 0.7608 1.267
## site102 1.1866 0.8428 0.8446 1.667
## site103 1.7254 0.5796 1.3539 2.199
## site104 1.4677 0.6813 1.1161 1.930
## site105 1.0879 0.9192 0.8442 1.402
## site106 1.1004 0.9088 0.8676 1.396
## site107 1.0482 0.9540 0.7016 1.566
## site108 0.6565 1.5233 0.3773 1.142
##
## Concordance= 0.776 (se = 0.01 )
## Likelihood ratio test= 1063 on 16 df, p=<2e-16
## Wald test = 672.9 on 16 df, p=<2e-16
## Score (logrank) test = 594.7 on 16 df, p=<2e-16
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ SS + dx_yr + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SS + dx_yr + site,
## data = All_IPF, id = ID, cluster = cohort)
##
## n= 123367, number of events= 2749
## (723 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## SS 0.030974 1.031458 0.125277 0.162608 0.190 0.848933
## dx_yr 0.103946 1.109540 0.006944 0.076371 1.361 0.173492
## site02R 0.630156 1.877903 0.368953 0.125183 5.034 4.81e-07 ***
## site03R -0.269560 0.763715 0.347486 0.045283 -5.953 2.64e-09 ***
## site04R -0.223640 0.799603 0.449486 0.094828 -2.358 0.018355 *
## site05R -0.323762 0.723422 0.368899 0.033701 -9.607 < 2e-16 ***
## site06R -0.040709 0.960109 0.325358 0.048845 -0.833 0.404604
## site07R -0.321802 0.724842 0.304787 0.108441 -2.968 0.003002 **
## site09R -0.038104 0.962613 0.339958 0.015775 -2.415 0.015717 *
## site1 0.247471 1.280782 0.281355 0.091382 2.708 0.006767 **
## site101 -0.107242 0.898308 0.293348 0.150615 -0.712 0.476447
## site102 -0.230252 0.794333 0.299009 0.146218 -1.575 0.115323
## site103 0.095934 1.100687 0.286336 0.081748 1.174 0.240579
## site104 -0.188759 0.827986 0.294693 0.089934 -2.099 0.035829 *
## site105 0.086678 1.090545 0.289693 0.158618 0.546 0.584752
## site106 -0.207888 0.812298 0.286322 0.102916 -2.020 0.043385 *
## site107 0.216668 1.241931 0.326836 0.168600 1.285 0.198759
## site108 0.156595 1.169521 0.370614 0.245100 0.639 0.522888
## site10R -0.175954 0.838657 0.398344 0.062455 -2.817 0.004843 **
## site11R -0.059040 0.942669 0.299373 0.064684 -0.913 0.361381
## site12R -0.096060 0.908410 0.334443 0.013505 -7.113 1.14e-12 ***
## site13R -0.296467 0.743440 0.301832 0.105809 -2.802 0.005080 **
## site14R -0.367835 0.692232 0.640763 0.125490 -2.931 0.003377 **
## site15R 0.030171 1.030631 0.331102 0.015587 1.936 0.052906 .
## site16R 0.136186 1.145895 0.344908 0.054401 2.503 0.012301 *
## site17R 0.046209 1.047293 0.360297 0.028774 1.606 0.108294
## site18R -0.266810 0.765819 0.322633 0.126094 -2.116 0.034348 *
## site19R -0.029385 0.971043 0.350014 0.021198 -1.386 0.165677
## site20R -0.268295 0.764682 0.347589 0.035996 -7.454 9.09e-14 ***
## site21R -0.309821 0.733578 0.319424 0.057242 -5.412 6.22e-08 ***
## site22R -0.072981 0.929618 0.305360 0.014113 -5.171 2.33e-07 ***
## site23R -0.164354 0.848441 0.322446 0.017296 -9.503 < 2e-16 ***
## site24R -0.161317 0.851023 0.331053 0.024477 -6.591 4.38e-11 ***
## site25R -0.056907 0.944682 0.354383 0.137682 -0.413 0.679373
## site26R -0.431052 0.649825 0.351739 0.024409 -17.660 < 2e-16 ***
## site27R -0.013442 0.986648 0.759717 0.124104 -0.108 0.913746
## site28R -0.137851 0.871229 0.433885 0.024705 -5.580 2.41e-08 ***
## site29R -0.316482 0.728708 0.435915 0.028476 -11.114 < 2e-16 ***
## site30R -0.211492 0.809376 0.330551 0.103844 -2.037 0.041687 *
## site31R -0.331768 0.717654 0.449720 0.091177 -3.639 0.000274 ***
## site32R -0.352554 0.702890 0.368485 0.038328 -9.198 < 2e-16 ***
## site33R -0.360796 0.697121 0.334123 0.044809 -8.052 8.15e-16 ***
## site34R -0.158377 0.853528 0.313520 0.054213 -2.921 0.003485 **
## site35R -0.117677 0.888983 0.333941 0.028404 -4.143 3.43e-05 ***
## site36R -0.217377 0.804627 0.337792 0.027174 -7.999 1.25e-15 ***
## site37R -0.182436 0.833238 0.333886 0.054554 -3.344 0.000825 ***
## site38R -0.296089 0.743721 0.328953 0.038789 -7.633 2.29e-14 ***
## site39R -0.214578 0.806882 0.360560 0.041030 -5.230 1.70e-07 ***
## site40R 0.159445 1.172860 0.344707 0.055151 2.891 0.003839 **
## site41R -0.217005 0.804926 0.368974 0.020661 -10.503 < 2e-16 ***
## site42R 0.082940 1.086477 0.359965 0.024748 3.351 0.000804 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SS 1.0315 0.9695 0.7500 1.4186
## dx_yr 1.1095 0.9013 0.9553 1.2887
## site02R 1.8779 0.5325 1.4693 2.4001
## site03R 0.7637 1.3094 0.6989 0.8346
## site04R 0.7996 1.2506 0.6640 0.9629
## site05R 0.7234 1.3823 0.6772 0.7728
## site06R 0.9601 1.0415 0.8725 1.0566
## site07R 0.7248 1.3796 0.5861 0.8965
## site09R 0.9626 1.0388 0.9333 0.9928
## site1 1.2808 0.7808 1.0708 1.5320
## site101 0.8983 1.1132 0.6687 1.2068
## site102 0.7943 1.2589 0.5964 1.0579
## site103 1.1007 0.9085 0.9377 1.2920
## site104 0.8280 1.2077 0.6942 0.9876
## site105 1.0905 0.9170 0.7991 1.4882
## site106 0.8123 1.2311 0.6639 0.9938
## site107 1.2419 0.8052 0.8925 1.7283
## site108 1.1695 0.8551 0.7234 1.8908
## site10R 0.8387 1.1924 0.7420 0.9479
## site11R 0.9427 1.0608 0.8304 1.0701
## site12R 0.9084 1.1008 0.8847 0.9328
## site13R 0.7434 1.3451 0.6042 0.9148
## site14R 0.6922 1.4446 0.5413 0.8853
## site15R 1.0306 0.9703 0.9996 1.0626
## site16R 1.1459 0.8727 1.0300 1.2748
## site17R 1.0473 0.9548 0.9899 1.1081
## site18R 0.7658 1.3058 0.5981 0.9805
## site19R 0.9710 1.0298 0.9315 1.0122
## site20R 0.7647 1.3077 0.7126 0.8206
## site21R 0.7336 1.3632 0.6557 0.8207
## site22R 0.9296 1.0757 0.9043 0.9557
## site23R 0.8484 1.1786 0.8202 0.8777
## site24R 0.8510 1.1751 0.8112 0.8928
## site25R 0.9447 1.0586 0.7213 1.2373
## site26R 0.6498 1.5389 0.6195 0.6817
## site27R 0.9866 1.0135 0.7736 1.2583
## site28R 0.8712 1.1478 0.8300 0.9145
## site29R 0.7287 1.3723 0.6892 0.7705
## site30R 0.8094 1.2355 0.6603 0.9921
## site31R 0.7177 1.3934 0.6002 0.8581
## site32R 0.7029 1.4227 0.6520 0.7577
## site33R 0.6971 1.4345 0.6385 0.7611
## site34R 0.8535 1.1716 0.7675 0.9492
## site35R 0.8890 1.1249 0.8408 0.9399
## site36R 0.8046 1.2428 0.7629 0.8486
## site37R 0.8332 1.2001 0.7487 0.9273
## site38R 0.7437 1.3446 0.6893 0.8025
## site39R 0.8069 1.2393 0.7445 0.8745
## site40R 1.1729 0.8526 1.0527 1.3067
## site41R 0.8049 1.2424 0.7730 0.8382
## site42R 1.0865 0.9204 1.0350 1.1405
##
## Concordance= 0.596 (se = 0.044 )
## Likelihood ratio test= 347.8 on 51 df, p=<2e-16
## Wald test = 242.9 on 51 df, p=<2e-16
## Score (logrank) test = 337.7 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ SS + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ SS + dx_yr + age_dx +
## sex + dich_Race + smokeHx + disadv + site, data = All_IPF,
## id = ID, cluster = cohort)
##
## n= 121241, number of events= 2709
## (2849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## SS 0.021623 1.021859 0.128121 0.160282 0.135 0.892684
## dx_yr 0.100773 1.106026 0.007308 0.081613 1.235 0.216917
## age_dx 0.004225 1.004234 0.002446 0.002173 1.944 0.051860 .
## sexF -0.158137 0.853733 0.044860 0.084978 -1.861 0.062755 .
## dich_RaceNon-White 0.016703 1.016844 0.068774 0.056977 0.293 0.769401
## smokeHxFormer 0.098784 1.103828 0.059392 0.036799 2.684 0.007265 **
## smokeHxAlways -0.031082 0.969396 0.138366 0.116463 -0.267 0.789561
## smokeHxUnknown 0.317872 1.374200 0.153713 0.231101 1.375 0.168986
## smokeHxEver 0.036053 1.036711 0.064255 0.008216 4.388 1.14e-05 ***
## disadv 0.043051 1.043991 0.070452 0.130195 0.331 0.740898
## site02R 0.629160 1.876034 0.380493 0.107660 5.844 5.10e-09 ***
## site03R -0.249026 0.779560 0.357495 0.056157 -4.434 9.23e-06 ***
## site04R -0.217899 0.804206 0.457644 0.115842 -1.881 0.059972 .
## site05R -0.320631 0.725691 0.379140 0.032248 -9.943 < 2e-16 ***
## site06R -0.054131 0.947308 0.337840 0.075797 -0.714 0.475132
## site07R -0.329551 0.719246 0.316328 0.107299 -3.071 0.002131 **
## site09R -0.084435 0.919031 0.352301 0.003247 -26.004 < 2e-16 ***
## site1 0.179764 1.196935 0.297461 0.129803 1.385 0.166083
## site101 -0.158936 0.853051 0.308266 0.137811 -1.153 0.248791
## site102 -0.301570 0.739656 0.314611 0.143872 -2.096 0.036073 *
## site103 0.020367 1.020575 0.302002 0.047936 0.425 0.670930
## site104 -0.254017 0.775679 0.310454 0.061098 -4.158 3.22e-05 ***
## site105 0.027700 1.028087 0.306629 0.143630 0.193 0.847073
## site106 -0.286677 0.750754 0.302123 0.084328 -3.400 0.000675 ***
## site107 0.122928 1.130803 0.342294 0.144849 0.849 0.396068
## site108 0.080164 1.083465 0.383060 0.225771 0.355 0.722538
## site10R -0.233663 0.791629 0.410039 0.084048 -2.780 0.005434 **
## site11R -0.092688 0.911478 0.313633 0.055257 -1.677 0.093462 .
## site12R -0.117967 0.888726 0.345460 0.007065 -16.696 < 2e-16 ***
## site13R -0.334295 0.715843 0.313297 0.107842 -3.100 0.001936 **
## site14R -0.373236 0.688503 0.646458 0.095411 -3.912 9.16e-05 ***
## site15R -0.008695 0.991343 0.343390 0.036035 -0.241 0.809327
## site16R 0.141014 1.151441 0.355026 0.059606 2.366 0.017992 *
## site17R 0.031445 1.031945 0.370900 0.032133 0.979 0.327786
## site18R -0.295970 0.743810 0.334789 0.101018 -2.930 0.003391 **
## site19R -0.072056 0.930479 0.360650 0.016226 -4.441 8.97e-06 ***
## site20R -0.266286 0.766220 0.358673 0.039675 -6.712 1.92e-11 ***
## site21R -0.348036 0.706073 0.330695 0.046107 -7.548 4.41e-14 ***
## site22R -0.082694 0.920633 0.317269 0.007929 -10.429 < 2e-16 ***
## site23R -0.221899 0.800996 0.336102 0.012504 -17.746 < 2e-16 ***
## site24R -0.174455 0.839915 0.342537 0.023248 -7.504 6.18e-14 ***
## site25R -0.061254 0.940584 0.364842 0.105983 -0.578 0.563290
## site26R -0.465184 0.628020 0.363114 0.040173 -11.580 < 2e-16 ***
## site27R 0.002709 1.002712 0.766945 0.141578 0.019 0.984737
## site28R -0.138738 0.870456 0.442462 0.012563 -11.044 < 2e-16 ***
## site29R -0.317423 0.728023 0.445291 0.026340 -12.051 < 2e-16 ***
## site30R -0.274692 0.759806 0.342643 0.097338 -2.822 0.004772 **
## site31R -0.368158 0.692008 0.457807 0.083588 -4.404 1.06e-05 ***
## site32R -0.394230 0.674199 0.388704 0.030653 -12.861 < 2e-16 ***
## site33R -0.398340 0.671434 0.347480 0.021494 -18.532 < 2e-16 ***
## site34R -0.173392 0.840808 0.326115 0.054937 -3.156 0.001598 **
## site35R -0.167913 0.845427 0.347040 0.016211 -10.358 < 2e-16 ***
## site36R -0.223628 0.799613 0.348817 0.017914 -12.483 < 2e-16 ***
## site37R -0.198711 0.819787 0.344636 0.061539 -3.229 0.001242 **
## site38R -0.302839 0.738718 0.340147 0.019941 -15.186 < 2e-16 ***
## site39R -0.257711 0.772819 0.370886 0.029439 -8.754 < 2e-16 ***
## site40R 0.119826 1.127300 0.354894 0.030382 3.944 8.02e-05 ***
## site41R -0.254612 0.775217 0.378313 0.013613 -18.704 < 2e-16 ***
## site42R 0.058738 1.060497 0.370001 0.034405 1.707 0.087774 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## SS 1.0219 0.9786 0.7464 1.3990
## dx_yr 1.1060 0.9041 0.9425 1.2979
## age_dx 1.0042 0.9958 1.0000 1.0085
## sexF 0.8537 1.1713 0.7228 1.0085
## dich_RaceNon-White 1.0168 0.9834 0.9094 1.1370
## smokeHxFormer 1.1038 0.9059 1.0270 1.1864
## smokeHxAlways 0.9694 1.0316 0.7716 1.2180
## smokeHxUnknown 1.3742 0.7277 0.8736 2.1615
## smokeHxEver 1.0367 0.9646 1.0202 1.0535
## disadv 1.0440 0.9579 0.8089 1.3475
## site02R 1.8760 0.5330 1.5191 2.3168
## site03R 0.7796 1.2828 0.6983 0.8703
## site04R 0.8042 1.2435 0.6409 1.0092
## site05R 0.7257 1.3780 0.6812 0.7730
## site06R 0.9473 1.0556 0.8165 1.0990
## site07R 0.7192 1.3903 0.5828 0.8876
## site09R 0.9190 1.0881 0.9132 0.9249
## site1 1.1969 0.8355 0.9281 1.5437
## site101 0.8531 1.1723 0.6511 1.1176
## site102 0.7397 1.3520 0.5579 0.9806
## site103 1.0206 0.9798 0.9291 1.1211
## site104 0.7757 1.2892 0.6881 0.8744
## site105 1.0281 0.9727 0.7758 1.3623
## site106 0.7508 1.3320 0.6364 0.8857
## site107 1.1308 0.8843 0.8513 1.5020
## site108 1.0835 0.9230 0.6960 1.6865
## site10R 0.7916 1.2632 0.6714 0.9334
## site11R 0.9115 1.0971 0.8179 1.0157
## site12R 0.8887 1.1252 0.8765 0.9011
## site13R 0.7158 1.3970 0.5795 0.8843
## site14R 0.6885 1.4524 0.5711 0.8301
## site15R 0.9913 1.0087 0.9237 1.0639
## site16R 1.1514 0.8685 1.0245 1.2941
## site17R 1.0319 0.9690 0.9690 1.0990
## site18R 0.7438 1.3444 0.6102 0.9067
## site19R 0.9305 1.0747 0.9014 0.9605
## site20R 0.7662 1.3051 0.7089 0.8282
## site21R 0.7061 1.4163 0.6451 0.7729
## site22R 0.9206 1.0862 0.9064 0.9351
## site23R 0.8010 1.2484 0.7816 0.8209
## site24R 0.8399 1.1906 0.8025 0.8791
## site25R 0.9406 1.0632 0.7642 1.1577
## site26R 0.6280 1.5923 0.5805 0.6795
## site27R 1.0027 0.9973 0.7597 1.3234
## site28R 0.8705 1.1488 0.8493 0.8922
## site29R 0.7280 1.3736 0.6914 0.7666
## site30R 0.7598 1.3161 0.6278 0.9195
## site31R 0.6920 1.4451 0.5874 0.8152
## site32R 0.6742 1.4832 0.6349 0.7159
## site33R 0.6714 1.4894 0.6437 0.7003
## site34R 0.8408 1.1893 0.7550 0.9364
## site35R 0.8454 1.1828 0.8190 0.8727
## site36R 0.7996 1.2506 0.7720 0.8282
## site37R 0.8198 1.2198 0.7266 0.9249
## site38R 0.7387 1.3537 0.7104 0.7682
## site39R 0.7728 1.2940 0.7295 0.8187
## site40R 1.1273 0.8871 1.0621 1.1965
## site41R 0.7752 1.2900 0.7548 0.7962
## site42R 1.0605 0.9430 0.9913 1.1345
##
## Concordance= 0.598 (se = 0.039 )
## Likelihood ratio test= 379.9 on 59 df, p=<2e-16
## Wald test = 880 on 59 df, p=<2e-16
## Score (logrank) test = 374.6 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ Soil + dx_yr, data=Simm_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ Soil + dx_yr,
## data = Simm_IPF, id = ID)
##
## n= 31982, number of events= 695
## (446 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Soil -0.063871 0.938126 0.280443 -0.228 0.81984
## dx_yr 0.023282 1.023555 0.008582 2.713 0.00667 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Soil 0.9381 1.066 0.5414 1.625
## dx_yr 1.0236 0.977 1.0065 1.041
##
## Concordance= 0.521 (se = 0.012 )
## Likelihood ratio test= 7.64 on 2 df, p=0.02
## Wald test = 7.62 on 2 df, p=0.02
## Score (logrank) test = 7.64 on 2 df, p=0.02
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ Soil + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv, data=Simm_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ Soil + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv, data = Simm_IPF,
## id = ID)
##
## n= 30488, number of events= 670
## (1940 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Soil -0.171177 0.842672 0.285003 -0.601 0.54810
## dx_yr 0.010725 1.010783 0.009161 1.171 0.24169
## age_dx 0.002084 1.002086 0.004566 0.456 0.64817
## sexF -0.385625 0.680026 0.088824 -4.341 1.42e-05 ***
## dich_RaceNon-White 0.157913 1.171065 0.128320 1.231 0.21846
## smokeHxFormer 0.090807 1.095058 0.091966 0.987 0.32345
## smokeHxAlways -0.423249 0.654916 0.266943 -1.586 0.11284
## smokeHxUnknown 0.514343 1.672540 0.170205 3.022 0.00251 **
## disadv 0.375604 1.455871 0.133916 2.805 0.00504 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Soil 0.8427 1.1867 0.4820 1.4732
## dx_yr 1.0108 0.9893 0.9928 1.0291
## age_dx 1.0021 0.9979 0.9932 1.0111
## sexF 0.6800 1.4705 0.5714 0.8093
## dich_RaceNon-White 1.1711 0.8539 0.9107 1.5059
## smokeHxFormer 1.0951 0.9132 0.9144 1.3113
## smokeHxAlways 0.6549 1.5269 0.3881 1.1051
## smokeHxUnknown 1.6725 0.5979 1.1981 2.3348
## disadv 1.4559 0.6869 1.1198 1.8928
##
## Concordance= 0.591 (se = 0.012 )
## Likelihood ratio test= 51.98 on 9 df, p=5e-08
## Wald test = 52.49 on 9 df, p=4e-08
## Score (logrank) test = 53.21 on 9 df, p=3e-08
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ Soil + dx_yr + site, data=PFF_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ Soil + dx_yr +
## site, data = PFF_IPF, id = ID)
##
## n= 54499, number of events= 1146
## (217 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Soil -0.13733 0.87168 0.19775 -0.694 0.4874
## dx_yr 0.10437 1.11001 0.01355 7.700 1.37e-14 ***
## site02R 0.68139 1.97661 0.33727 2.020 0.0434 *
## site03R -0.15200 0.85899 0.37177 -0.409 0.6826
## site04R -0.17379 0.84048 0.45210 -0.384 0.7007
## site05R -0.26063 0.77057 0.37271 -0.699 0.4844
## site06R 0.01516 1.01528 0.32954 0.046 0.9633
## site07R -0.26505 0.76717 0.30966 -0.856 0.3920
## site08R NA NA 0.00000 NA NA
## site09R -0.01294 0.98714 0.33974 -0.038 0.9696
## site10R -0.04774 0.95339 0.41012 -0.116 0.9073
## site11R 0.01097 1.01103 0.31503 0.035 0.9722
## site12R -0.05919 0.94253 0.33532 -0.177 0.8599
## site13R -0.27381 0.76048 0.30251 -0.905 0.3654
## site14R -0.34498 0.70823 0.64120 -0.538 0.5906
## site15R 0.07651 1.07951 0.33385 0.229 0.8187
## site16R 0.15493 1.16758 0.34484 0.449 0.6532
## site17R 0.09730 1.10219 0.36834 0.264 0.7917
## site18R -0.18592 0.83034 0.34179 -0.544 0.5865
## site19R 0.15499 1.16765 0.42179 0.367 0.7133
## site20R -0.18301 0.83276 0.35775 -0.512 0.6090
## site21R -0.22577 0.79791 0.32849 -0.687 0.4919
## site22R -0.04354 0.95739 0.30546 -0.143 0.8866
## site23R -0.03103 0.96944 0.36591 -0.085 0.9324
## site24R -0.11852 0.88823 0.33176 -0.357 0.7209
## site25R 0.01875 1.01892 0.32670 0.057 0.9542
## site26R -0.26606 0.76640 0.39041 -0.681 0.4956
## site27R 0.02740 1.02778 0.75985 0.036 0.9712
## site28R -0.08049 0.92266 0.43583 -0.185 0.8535
## site29R -0.27336 0.76082 0.43374 -0.630 0.5285
## site30R -0.15047 0.86030 0.33782 -0.445 0.6560
## site31R -0.30214 0.73924 0.44963 -0.672 0.5016
## site32R -0.10390 0.90132 0.50199 -0.207 0.8360
## site33R -0.19395 0.82370 0.38479 -0.504 0.6142
## site34R -0.13289 0.87556 0.31355 -0.424 0.6717
## site35R -0.11603 0.89045 0.33429 -0.347 0.7285
## site36R -0.15534 0.85612 0.34128 -0.455 0.6490
## site37R -0.14982 0.86086 0.33414 -0.448 0.6539
## site38R -0.26203 0.76949 0.32968 -0.795 0.4267
## site39R -0.12656 0.88112 0.37466 -0.338 0.7355
## site40R 0.27915 1.32201 0.37468 0.745 0.4562
## site41R -0.17072 0.84305 0.37054 -0.461 0.6450
## site42R 0.09619 1.10097 0.36030 0.267 0.7895
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Soil 0.8717 1.1472 0.5916 1.284
## dx_yr 1.1100 0.9009 1.0809 1.140
## site02R 1.9766 0.5059 1.0206 3.828
## site03R 0.8590 1.1642 0.4145 1.780
## site04R 0.8405 1.1898 0.3465 2.039
## site05R 0.7706 1.2977 0.3712 1.600
## site06R 1.0153 0.9850 0.5322 1.937
## site07R 0.7672 1.3035 0.4181 1.408
## site08R NA NA NA NA
## site09R 0.9871 1.0130 0.5072 1.921
## site10R 0.9534 1.0489 0.4268 2.130
## site11R 1.0110 0.9891 0.5453 1.875
## site12R 0.9425 1.0610 0.4885 1.819
## site13R 0.7605 1.3150 0.4203 1.376
## site14R 0.7082 1.4120 0.2015 2.489
## site15R 1.0795 0.9263 0.5611 2.077
## site16R 1.1676 0.8565 0.5940 2.295
## site17R 1.1022 0.9073 0.5355 2.269
## site18R 0.8303 1.2043 0.4249 1.623
## site19R 1.1676 0.8564 0.5108 2.669
## site20R 0.8328 1.2008 0.4130 1.679
## site21R 0.7979 1.2533 0.4191 1.519
## site22R 0.9574 1.0445 0.5261 1.742
## site23R 0.9694 1.0315 0.4732 1.986
## site24R 0.8882 1.1258 0.4636 1.702
## site25R 1.0189 0.9814 0.5371 1.933
## site26R 0.7664 1.3048 0.3566 1.647
## site27R 1.0278 0.9730 0.2318 4.557
## site28R 0.9227 1.0838 0.3927 2.168
## site29R 0.7608 1.3144 0.3251 1.780
## site30R 0.8603 1.1624 0.4437 1.668
## site31R 0.7392 1.3527 0.3062 1.784
## site32R 0.9013 1.1095 0.3370 2.411
## site33R 0.8237 1.2140 0.3875 1.751
## site34R 0.8756 1.1421 0.4736 1.619
## site35R 0.8904 1.1230 0.4624 1.715
## site36R 0.8561 1.1681 0.4386 1.671
## site37R 0.8609 1.1616 0.4472 1.657
## site38R 0.7695 1.2996 0.4033 1.468
## site39R 0.8811 1.1349 0.4228 1.836
## site40R 1.3220 0.7564 0.6343 2.755
## site41R 0.8431 1.1862 0.4078 1.743
## site42R 1.1010 0.9083 0.5434 2.231
##
## Concordance= 0.59 (se = 0.009 )
## Likelihood ratio test= 111 on 42 df, p=4e-08
## Wald test = 109.2 on 42 df, p=7e-08
## Score (logrank) test = 113.5 on 42 df, p=2e-08
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ Soil + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=PFF_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ Soil + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = PFF_IPF,
## id = ID)
##
## n= 53867, number of events= 1131
## (849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Soil -0.0934647 0.9107701 0.2028037 -0.461 0.6449
## dx_yr 0.1029613 1.1084485 0.0139904 7.359 1.85e-13 ***
## age_dx -0.0003036 0.9996964 0.0039352 -0.077 0.9385
## sexM 0.0616450 1.0635847 0.0713035 0.865 0.3873
## dich_RaceNon-White -0.0553269 0.9461758 0.1260294 -0.439 0.6607
## smokeHxEver 0.0432725 1.0442224 0.0646213 0.670 0.5031
## disadv -0.0468679 0.9542134 0.1128079 -0.415 0.6778
## site02R 0.6313496 1.8801463 0.3495795 1.806 0.0709 .
## site03R -0.2051793 0.8145012 0.3855647 -0.532 0.5946
## site04R -0.2309114 0.7938098 0.4626797 -0.499 0.6177
## site05R -0.3102742 0.7332459 0.3850124 -0.806 0.4203
## site06R -0.0277814 0.9726009 0.3448652 -0.081 0.9358
## site07R -0.3132185 0.7310901 0.3238172 -0.967 0.3334
## site08R NA NA 0.0000000 NA NA
## site09R -0.0859959 0.9175980 0.3538756 -0.243 0.8080
## site10R -0.1303326 0.8778035 0.4247409 -0.307 0.7590
## site11R -0.0612369 0.9406004 0.3330850 -0.184 0.8541
## site12R -0.1040231 0.9012045 0.3491777 -0.298 0.7658
## site13R -0.3273849 0.7208062 0.3155908 -1.037 0.2996
## site14R -0.3983486 0.6714279 0.6482392 -0.615 0.5389
## site15R 0.0234922 1.0237703 0.3492368 0.067 0.9464
## site16R 0.1127367 1.1193372 0.3560721 0.317 0.7515
## site17R 0.0297750 1.0302227 0.3819637 0.078 0.9379
## site18R -0.2395566 0.7869767 0.3572108 -0.671 0.5025
## site19R 0.0616245 1.0635629 0.4376932 0.141 0.8880
## site20R -0.2468888 0.7812276 0.3722964 -0.663 0.5072
## site21R -0.2793362 0.7562856 0.3423697 -0.816 0.4146
## site22R -0.0925218 0.9116293 0.3193501 -0.290 0.7720
## site23R -0.1267154 0.8809844 0.3827611 -0.331 0.7406
## site24R -0.1727503 0.8413477 0.3454764 -0.500 0.6171
## site25R -0.0139625 0.9861346 0.3399094 -0.041 0.9672
## site26R -0.3352965 0.7151260 0.4042842 -0.829 0.4069
## site27R -0.0712275 0.9312500 0.7705994 -0.092 0.9264
## site28R -0.1142890 0.8920001 0.4470131 -0.256 0.7982
## site29R -0.3194236 0.7265677 0.4448285 -0.718 0.4727
## site30R -0.2134605 0.8077841 0.3512371 -0.608 0.5434
## site31R -0.3485804 0.7056892 0.4585690 -0.760 0.4472
## site32R -0.2275125 0.7965125 0.5331045 -0.427 0.6695
## site33R -0.2733910 0.7607952 0.4005499 -0.683 0.4949
## site34R -0.1706071 0.8431528 0.3285899 -0.519 0.6036
## site35R -0.1655837 0.8473989 0.3481936 -0.476 0.6344
## site36R -0.1918885 0.8253989 0.3554542 -0.540 0.5893
## site37R -0.1890942 0.8277085 0.3465908 -0.546 0.5854
## site38R -0.2985583 0.7418870 0.3429988 -0.870 0.3841
## site39R -0.1944922 0.8232526 0.3881310 -0.501 0.6163
## site40R 0.2195829 1.2455571 0.3863876 0.568 0.5698
## site41R -0.2117426 0.8091729 0.3808945 -0.556 0.5783
## site42R 0.0349380 1.0355555 0.3721420 0.094 0.9252
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Soil 0.9108 1.0980 0.6120 1.355
## dx_yr 1.1084 0.9022 1.0785 1.139
## age_dx 0.9997 1.0003 0.9920 1.007
## sexM 1.0636 0.9402 0.9249 1.223
## dich_RaceNon-White 0.9462 1.0569 0.7391 1.211
## smokeHxEver 1.0442 0.9577 0.9200 1.185
## disadv 0.9542 1.0480 0.7649 1.190
## site02R 1.8801 0.5319 0.9476 3.730
## site03R 0.8145 1.2277 0.3826 1.734
## site04R 0.7938 1.2597 0.3205 1.966
## site05R 0.7332 1.3638 0.3448 1.559
## site06R 0.9726 1.0282 0.4947 1.912
## site07R 0.7311 1.3678 0.3876 1.379
## site08R NA NA NA NA
## site09R 0.9176 1.0898 0.4586 1.836
## site10R 0.8778 1.1392 0.3818 2.018
## site11R 0.9406 1.0632 0.4896 1.807
## site12R 0.9012 1.1096 0.4546 1.787
## site13R 0.7208 1.3873 0.3883 1.338
## site14R 0.6714 1.4894 0.1885 2.392
## site15R 1.0238 0.9768 0.5163 2.030
## site16R 1.1193 0.8934 0.5570 2.249
## site17R 1.0302 0.9707 0.4873 2.178
## site18R 0.7870 1.2707 0.3908 1.585
## site19R 1.0636 0.9402 0.4510 2.508
## site20R 0.7812 1.2800 0.3766 1.621
## site21R 0.7563 1.3223 0.3866 1.479
## site22R 0.9116 1.0969 0.4875 1.705
## site23R 0.8810 1.1351 0.4161 1.865
## site24R 0.8413 1.1886 0.4275 1.656
## site25R 0.9861 1.0141 0.5065 1.920
## site26R 0.7151 1.3984 0.3238 1.579
## site27R 0.9312 1.0738 0.2057 4.217
## site28R 0.8920 1.1211 0.3714 2.142
## site29R 0.7266 1.3763 0.3038 1.737
## site30R 0.8078 1.2380 0.4058 1.608
## site31R 0.7057 1.4171 0.2873 1.734
## site32R 0.7965 1.2555 0.2802 2.264
## site33R 0.7608 1.3144 0.3470 1.668
## site34R 0.8432 1.1860 0.4428 1.605
## site35R 0.8474 1.1801 0.4283 1.677
## site36R 0.8254 1.2115 0.4112 1.657
## site37R 0.8277 1.2082 0.4196 1.633
## site38R 0.7419 1.3479 0.3788 1.453
## site39R 0.8233 1.2147 0.3847 1.762
## site40R 1.2456 0.8029 0.5841 2.656
## site41R 0.8092 1.2358 0.3836 1.707
## site42R 1.0356 0.9657 0.4994 2.148
##
## Concordance= 0.591 (se = 0.009 )
## Likelihood ratio test= 111.4 on 47 df, p=4e-07
## Wald test = 109.9 on 47 df, p=6e-07
## Score (logrank) test = 114.3 on 47 df, p=2e-07
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ Soil + dx_yr + site, data=CARE_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ Soil + dx_yr +
## site, data = CARE_IPF, id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Soil -0.41584 0.65979 0.24801 -1.677 0.09360 .
## dx_yr 0.81730 2.26437 0.03233 25.282 < 2e-16 ***
## site102 0.17519 1.19148 0.14943 1.172 0.24102
## site103 0.41989 1.52179 0.13316 3.153 0.00161 **
## site104 0.28207 1.32587 0.14910 1.892 0.05851 .
## site105 0.01151 1.01157 0.12992 0.089 0.92943
## site106 0.16798 1.18291 0.12222 1.374 0.16931
## site107 0.05400 1.05549 0.19968 0.270 0.78681
## site108 -0.42330 0.65488 0.26383 -1.604 0.10861
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Soil 0.6598 1.5156 0.4058 1.073
## dx_yr 2.2644 0.4416 2.1254 2.412
## site102 1.1915 0.8393 0.8890 1.597
## site103 1.5218 0.6571 1.1722 1.976
## site104 1.3259 0.7542 0.9899 1.776
## site105 1.0116 0.9886 0.7842 1.305
## site106 1.1829 0.8454 0.9309 1.503
## site107 1.0555 0.9474 0.7136 1.561
## site108 0.6549 1.5270 0.3905 1.098
##
## Concordance= 0.774 (se = 0.01 )
## Likelihood ratio test= 1055 on 9 df, p=<2e-16
## Wald test = 669.2 on 9 df, p=<2e-16
## Score (logrank) test = 590.4 on 9 df, p=<2e-16
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ Soil + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site, data=CARE_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ Soil + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = CARE_IPF,
## id = ID)
##
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## Soil -0.520070 0.594479 0.255310 -2.037 0.04165 *
## dx_yr 0.817926 2.265796 0.032507 25.162 < 2e-16 ***
## age_dx 0.012881 1.012965 0.004519 2.850 0.00437 **
## sexF -0.042562 0.958331 0.077062 -0.552 0.58073
## dich_RaceNon-White -0.142358 0.867311 0.110439 -1.289 0.19739
## smokeHxFormer 0.004960 1.004972 0.081392 0.061 0.95141
## smokeHxAlways 0.064492 1.066617 0.169049 0.381 0.70283
## smokeHxUnknown 0.438159 1.549851 0.734863 0.596 0.55101
## disadv 0.025556 1.025885 0.131460 0.194 0.84586
## site102 0.149268 1.160985 0.151745 0.984 0.32527
## site103 0.408201 1.504109 0.137222 2.975 0.00293 **
## site104 0.265885 1.304585 0.150802 1.763 0.07788 .
## site105 0.015099 1.015214 0.133367 0.113 0.90986
## site106 0.148186 1.159728 0.123647 1.198 0.23074
## site107 -0.028714 0.971694 0.207966 -0.138 0.89018
## site108 -0.432675 0.648771 0.267380 -1.618 0.10562
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Soil 0.5945 1.6821 0.3604 0.9805
## dx_yr 2.2658 0.4413 2.1259 2.4149
## age_dx 1.0130 0.9872 1.0040 1.0220
## sexF 0.9583 1.0435 0.8240 1.1146
## dich_RaceNon-White 0.8673 1.1530 0.6985 1.0769
## smokeHxFormer 1.0050 0.9951 0.8568 1.1788
## smokeHxAlways 1.0666 0.9375 0.7658 1.4856
## smokeHxUnknown 1.5499 0.6452 0.3671 6.5434
## disadv 1.0259 0.9748 0.7929 1.3274
## site102 1.1610 0.8613 0.8623 1.5631
## site103 1.5041 0.6648 1.1494 1.9683
## site104 1.3046 0.7665 0.9708 1.7532
## site105 1.0152 0.9850 0.7817 1.3185
## site106 1.1597 0.8623 0.9101 1.4778
## site107 0.9717 1.0291 0.6464 1.4607
## site108 0.6488 1.5414 0.3841 1.0957
##
## Concordance= 0.776 (se = 0.01 )
## Likelihood ratio test= 1068 on 16 df, p=<2e-16
## Wald test = 677.4 on 16 df, p=<2e-16
## Score (logrank) test = 596.7 on 16 df, p=<2e-16
coxPH_model1 <- coxph(Surv(start, end, event==1) ~ Soil + dx_yr + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model1)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ Soil + dx_yr +
## site, data = All_IPF, id = ID, cluster = cohort)
##
## n= 123367, number of events= 2749
## (723 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## Soil 0.096461 1.101266 0.133647 0.133849 0.721 0.471116
## dx_yr 0.104064 1.109671 0.006936 0.075734 1.374 0.169420
## site02R 0.652506 1.920348 0.335113 0.078593 8.302 < 2e-16 ***
## site03R -0.340474 0.711433 0.359238 0.113012 -3.013 0.002589 **
## site04R -0.248483 0.779983 0.450553 0.096029 -2.588 0.009666 **
## site05R -0.354093 0.701809 0.370356 0.055673 -6.360 2.01e-10 ***
## site06R -0.070708 0.931734 0.326867 0.085224 -0.830 0.406723
## site07R -0.349283 0.705194 0.307018 0.116545 -2.997 0.002727 **
## site09R -0.034744 0.965853 0.339721 0.019175 -1.812 0.069995 .
## site1 0.225422 1.252851 0.282632 0.109365 2.061 0.039285 *
## site101 -0.125572 0.881992 0.294226 0.145474 -0.863 0.388031
## site102 -0.243851 0.783604 0.298041 0.113797 -2.143 0.032125 *
## site103 0.103426 1.108964 0.286428 0.086177 1.200 0.230078
## site104 -0.184790 0.831279 0.294742 0.088643 -2.085 0.037100 *
## site105 0.080071 1.083364 0.289690 0.149090 0.537 0.591224
## site106 -0.234705 0.790804 0.288592 0.108516 -2.163 0.030552 *
## site107 0.210863 1.234743 0.326842 0.160289 1.316 0.188338
## site108 0.138246 1.148258 0.369865 0.208500 0.663 0.507297
## site10R -0.219847 0.802642 0.396887 0.122056 -1.801 0.071673 .
## site11R -0.106694 0.898801 0.306454 0.093755 -1.138 0.255118
## site12R -0.115310 0.891090 0.334434 0.037397 -3.083 0.002047 **
## site13R -0.305440 0.736799 0.301995 0.101017 -3.024 0.002498 **
## site14R -0.368476 0.691788 0.640729 0.116835 -3.154 0.001612 **
## site15R 0.012552 1.012631 0.331923 0.039339 0.319 0.749672
## site16R 0.132608 1.141802 0.344574 0.032946 4.025 5.70e-05 ***
## site17R 0.004196 1.004205 0.363788 0.054377 0.077 0.938486
## site18R -0.315793 0.729210 0.330579 0.176975 -1.784 0.074359 .
## site19R -0.147884 0.862531 0.385297 0.176120 -0.840 0.401090
## site20R -0.314603 0.730079 0.351839 0.077011 -4.085 4.40e-05 ***
## site21R -0.348625 0.705657 0.323476 0.085881 -4.059 4.92e-05 ***
## site22R -0.076702 0.926165 0.305238 0.021875 -3.506 0.000454 ***
## site23R -0.248213 0.780193 0.342939 0.126912 -1.956 0.050489 .
## site24R -0.176336 0.838336 0.330958 0.052781 -3.341 0.000835 ***
## site25R -0.038912 0.961835 0.325702 0.059452 -0.655 0.512779
## site26R -0.502636 0.604934 0.365918 0.160685 -3.128 0.001760 **
## site27R -0.018661 0.981512 0.759684 0.113857 -0.164 0.869809
## site28R -0.161772 0.850635 0.434634 0.048203 -3.356 0.000791 ***
## site29R -0.305843 0.736502 0.433702 0.047544 -6.433 1.25e-10 ***
## site30R -0.244573 0.783039 0.333738 0.119179 -2.052 0.040155 *
## site31R -0.340651 0.711307 0.449483 0.071989 -4.732 2.22e-06 ***
## site32R -0.520670 0.594122 0.436103 0.244481 -2.130 0.033196 *
## site33R -0.446444 0.639900 0.357086 0.171583 -2.602 0.009271 **
## site34R -0.162833 0.849733 0.313460 0.045958 -3.543 0.000395 ***
## site35R -0.106809 0.898697 0.334019 0.037740 -2.830 0.004653 **
## site36R -0.243487 0.783889 0.339251 0.052750 -4.616 3.91e-06 ***
## site37R -0.189396 0.827459 0.333946 0.051262 -3.695 0.000220 ***
## site38R -0.306910 0.735716 0.329243 0.042347 -7.248 4.24e-13 ***
## site39R -0.270550 0.762959 0.366888 0.085432 -3.167 0.001541 **
## site40R 0.095661 1.100386 0.357236 0.153517 0.623 0.533197
## site41R -0.231058 0.793693 0.369392 0.055471 -4.165 3.11e-05 ***
## site42R 0.076538 1.079544 0.360078 0.027902 2.743 0.006086 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Soil 1.1013 0.9080 0.8471 1.4316
## dx_yr 1.1097 0.9012 0.9566 1.2872
## site02R 1.9203 0.5207 1.6462 2.2402
## site03R 0.7114 1.4056 0.5701 0.8878
## site04R 0.7800 1.2821 0.6462 0.9415
## site05R 0.7018 1.4249 0.6293 0.7827
## site06R 0.9317 1.0733 0.7884 1.1011
## site07R 0.7052 1.4180 0.5612 0.8862
## site09R 0.9659 1.0354 0.9302 1.0028
## site1 1.2529 0.7982 1.0111 1.5524
## site101 0.8820 1.1338 0.6632 1.1730
## site102 0.7836 1.2762 0.6269 0.9794
## site103 1.1090 0.9017 0.9366 1.3130
## site104 0.8313 1.2030 0.6987 0.9890
## site105 1.0834 0.9231 0.8089 1.4510
## site106 0.7908 1.2645 0.6393 0.9782
## site107 1.2347 0.8099 0.9019 1.6905
## site108 1.1483 0.8709 0.7631 1.7279
## site10R 0.8026 1.2459 0.6319 1.0196
## site11R 0.8988 1.1126 0.7479 1.0801
## site12R 0.8911 1.1222 0.8281 0.9589
## site13R 0.7368 1.3572 0.6045 0.8981
## site14R 0.6918 1.4455 0.5502 0.8698
## site15R 1.0126 0.9875 0.9375 1.0938
## site16R 1.1418 0.8758 1.0704 1.2180
## site17R 1.0042 0.9958 0.9027 1.1171
## site18R 0.7292 1.3713 0.5155 1.0316
## site19R 0.8625 1.1594 0.6107 1.2181
## site20R 0.7301 1.3697 0.6278 0.8490
## site21R 0.7057 1.4171 0.5963 0.8350
## site22R 0.9262 1.0797 0.8873 0.9667
## site23R 0.7802 1.2817 0.6084 1.0005
## site24R 0.8383 1.1928 0.7559 0.9297
## site25R 0.9618 1.0397 0.8560 1.0807
## site26R 0.6049 1.6531 0.4415 0.8289
## site27R 0.9815 1.0188 0.7852 1.2269
## site28R 0.8506 1.1756 0.7740 0.9349
## site29R 0.7365 1.3578 0.6710 0.8084
## site30R 0.7830 1.2771 0.6199 0.9891
## site31R 0.7113 1.4059 0.6177 0.8191
## site32R 0.5941 1.6832 0.3679 0.9594
## site33R 0.6399 1.5627 0.4572 0.8957
## site34R 0.8497 1.1768 0.7765 0.9298
## site35R 0.8987 1.1127 0.8346 0.9677
## site36R 0.7839 1.2757 0.7069 0.8693
## site37R 0.8275 1.2085 0.7484 0.9149
## site38R 0.7357 1.3592 0.6771 0.7994
## site39R 0.7630 1.3107 0.6453 0.9020
## site40R 1.1004 0.9088 0.8145 1.4867
## site41R 0.7937 1.2599 0.7119 0.8848
## site42R 1.0795 0.9263 1.0221 1.1402
##
## Concordance= 0.595 (se = 0.043 )
## Likelihood ratio test= 348.3 on 51 df, p=<2e-16
## Wald test = 2.32 on 51 df, p=1
## Score (logrank) test = 337.7 on 51 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
coxPH_model2 <- coxph(Surv(start, end, event==1) ~ Soil + dx_yr + age_dx + sex + dich_Race + smokeHx + disadv + site + cluster(cohort), data=All_IPF, id=ID)
summary(coxPH_model2)
## Call:
## coxph(formula = Surv(start, end, event == 1) ~ Soil + dx_yr +
## age_dx + sex + dich_Race + smokeHx + disadv + site, data = All_IPF,
## id = ID, cluster = cohort)
##
## n= 121241, number of events= 2709
## (2849 observations deleted due to missingness)
##
## coef exp(coef) se(coef) robust se z Pr(>|z|)
## Soil 0.106677 1.112575 0.136467 0.117733 0.906 0.364884
## dx_yr 0.100900 1.106166 0.007300 0.080994 1.246 0.212852
## age_dx 0.004224 1.004233 0.002446 0.002114 1.998 0.045750 *
## sexF -0.157481 0.854293 0.044865 0.084406 -1.866 0.062076 .
## dich_RaceNon-White 0.016495 1.016632 0.068702 0.060435 0.273 0.784901
## smokeHxFormer 0.098347 1.103346 0.059368 0.038470 2.556 0.010574 *
## smokeHxAlways -0.029778 0.970661 0.138353 0.111049 -0.268 0.788584
## smokeHxUnknown 0.316017 1.371653 0.153680 0.235887 1.340 0.180344
## smokeHxEver 0.036472 1.037145 0.064269 0.008234 4.430 9.44e-06 ***
## disadv 0.036568 1.037245 0.070853 0.129685 0.282 0.777961
## site02R 0.637105 1.890998 0.345770 0.101831 6.256 3.94e-10 ***
## site03R -0.326363 0.721543 0.369836 0.097033 -3.363 0.000770 ***
## site04R -0.245931 0.781976 0.458933 0.119661 -2.055 0.039857 *
## site05R -0.353007 0.702572 0.380812 0.038970 -9.059 < 2e-16 ***
## site06R -0.084278 0.919176 0.339367 0.092366 -0.912 0.361540
## site07R -0.359488 0.698033 0.318634 0.121674 -2.955 0.003131 **
## site09R -0.083154 0.920209 0.351989 0.015139 -5.493 3.95e-08 ***
## site1 0.156513 1.169426 0.298746 0.134484 1.164 0.244504
## site101 -0.178533 0.836497 0.309186 0.138464 -1.289 0.197267
## site102 -0.314564 0.730107 0.313739 0.113777 -2.765 0.005697 **
## site103 0.028942 1.029365 0.302123 0.050852 0.569 0.569266
## site104 -0.248756 0.779770 0.310535 0.060744 -4.095 4.22e-05 ***
## site105 0.021678 1.021914 0.306668 0.137299 0.158 0.874546
## site106 -0.316017 0.729047 0.304411 0.097004 -3.258 0.001123 **
## site107 0.117805 1.125025 0.342336 0.139086 0.847 0.396997
## site108 0.063543 1.065606 0.382243 0.193529 0.328 0.742655
## site10R -0.291765 0.746944 0.407767 0.115284 -2.531 0.011379 *
## site11R -0.144870 0.865135 0.320638 0.088383 -1.639 0.101188
## site12R -0.137166 0.871825 0.345621 0.027984 -4.902 9.51e-07 ***
## site13R -0.344672 0.708452 0.313547 0.108239 -3.184 0.001451 **
## site14R -0.377135 0.685824 0.646436 0.081436 -4.631 3.64e-06 ***
## site15R -0.029217 0.971206 0.343951 0.030594 -0.955 0.339584
## site16R 0.134074 1.143477 0.354646 0.027724 4.836 1.33e-06 ***
## site17R -0.012730 0.987350 0.374477 0.037667 -0.338 0.735384
## site18R -0.351510 0.703625 0.342338 0.168828 -2.082 0.037338 *
## site19R -0.202751 0.816481 0.397030 0.155643 -1.303 0.192687
## site20R -0.316339 0.728812 0.363331 0.057677 -5.485 4.14e-08 ***
## site21R -0.390065 0.677013 0.334837 0.080963 -4.818 1.45e-06 ***
## site22R -0.089115 0.914740 0.317119 0.026983 -3.303 0.000958 ***
## site23R -0.314688 0.730017 0.356663 0.110430 -2.850 0.004377 **
## site24R -0.189903 0.827039 0.342612 0.041268 -4.602 4.19e-06 ***
## site25R -0.057732 0.943903 0.337658 0.079963 -0.722 0.470304
## site26R -0.550175 0.576849 0.376581 0.150283 -3.661 0.000251 ***
## site27R -0.004913 0.995099 0.766972 0.135581 -0.036 0.971093
## site28R -0.163375 0.849272 0.443301 0.032192 -5.075 3.88e-07 ***
## site29R -0.311727 0.732181 0.442859 0.084305 -3.698 0.000218 ***
## site30R -0.312130 0.731886 0.345998 0.124065 -2.516 0.011874 *
## site31R -0.376698 0.686123 0.457649 0.068061 -5.535 3.12e-08 ***
## site32R -0.584303 0.557494 0.459060 0.225207 -2.595 0.009472 **
## site33R -0.494475 0.609891 0.369430 0.158082 -3.128 0.001760 **
## site34R -0.177892 0.837033 0.326118 0.049431 -3.599 0.000320 ***
## site35R -0.157731 0.854080 0.346996 0.025635 -6.153 7.60e-10 ***
## site36R -0.250990 0.778030 0.350342 0.040173 -6.248 4.17e-10 ***
## site37R -0.206351 0.813547 0.344747 0.060862 -3.390 0.000698 ***
## site38R -0.314105 0.730443 0.340452 0.026105 -12.032 < 2e-16 ***
## site39R -0.317998 0.727604 0.377636 0.075814 -4.194 2.74e-05 ***
## site40R 0.045278 1.046318 0.367447 0.147314 0.307 0.758573
## site41R -0.272098 0.761780 0.378653 0.054037 -5.035 4.77e-07 ***
## site42R 0.050405 1.051697 0.370161 0.038905 1.296 0.195121
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## Soil 1.1126 0.8988 0.8833 1.4013
## dx_yr 1.1062 0.9040 0.9438 1.2965
## age_dx 1.0042 0.9958 1.0001 1.0084
## sexF 0.8543 1.1706 0.7240 1.0080
## dich_RaceNon-White 1.0166 0.9836 0.9031 1.1445
## smokeHxFormer 1.1033 0.9063 1.0232 1.1898
## smokeHxAlways 0.9707 1.0302 0.7808 1.2067
## smokeHxUnknown 1.3717 0.7290 0.8639 2.1779
## smokeHxEver 1.0371 0.9642 1.0205 1.0540
## disadv 1.0372 0.9641 0.8044 1.3374
## site02R 1.8910 0.5288 1.5489 2.3087
## site03R 0.7215 1.3859 0.5966 0.8727
## site04R 0.7820 1.2788 0.6185 0.9887
## site05R 0.7026 1.4233 0.6509 0.7583
## site06R 0.9192 1.0879 0.7670 1.1016
## site07R 0.6980 1.4326 0.5499 0.8860
## site09R 0.9202 1.0867 0.8933 0.9479
## site1 1.1694 0.8551 0.8985 1.5221
## site101 0.8365 1.1955 0.6377 1.0973
## site102 0.7301 1.3697 0.5842 0.9125
## site103 1.0294 0.9715 0.9317 1.1372
## site104 0.7798 1.2824 0.6922 0.8784
## site105 1.0219 0.9786 0.7808 1.3375
## site106 0.7290 1.3717 0.6028 0.8817
## site107 1.1250 0.8889 0.8566 1.4776
## site108 1.0656 0.9384 0.7292 1.5571
## site10R 0.7469 1.3388 0.5959 0.9363
## site11R 0.8651 1.1559 0.7275 1.0288
## site12R 0.8718 1.1470 0.8253 0.9210
## site13R 0.7085 1.4115 0.5730 0.8759
## site14R 0.6858 1.4581 0.5846 0.8045
## site15R 0.9712 1.0296 0.9147 1.0312
## site16R 1.1435 0.8745 1.0830 1.2073
## site17R 0.9874 1.0128 0.9171 1.0630
## site18R 0.7036 1.4212 0.5054 0.9796
## site19R 0.8165 1.2248 0.6018 1.1077
## site20R 0.7288 1.3721 0.6509 0.8160
## site21R 0.6770 1.4771 0.5777 0.7934
## site22R 0.9147 1.0932 0.8676 0.9644
## site23R 0.7300 1.3698 0.5879 0.9064
## site24R 0.8270 1.2091 0.7628 0.8967
## site25R 0.9439 1.0594 0.8070 1.1041
## site26R 0.5768 1.7336 0.4297 0.7744
## site27R 0.9951 1.0049 0.7629 1.2980
## site28R 0.8493 1.1775 0.7973 0.9046
## site29R 0.7322 1.3658 0.6207 0.8637
## site30R 0.7319 1.3663 0.5739 0.9334
## site31R 0.6861 1.4575 0.6004 0.7840
## site32R 0.5575 1.7937 0.3585 0.8668
## site33R 0.6099 1.6396 0.4474 0.8314
## site34R 0.8370 1.1947 0.7597 0.9222
## site35R 0.8541 1.1709 0.8122 0.8981
## site36R 0.7780 1.2853 0.7191 0.8418
## site37R 0.8135 1.2292 0.7221 0.9166
## site38R 0.7304 1.3690 0.6940 0.7688
## site39R 0.7276 1.3744 0.6271 0.8442
## site40R 1.0463 0.9557 0.7839 1.3966
## site41R 0.7618 1.3127 0.6852 0.8469
## site42R 1.0517 0.9508 0.9745 1.1350
##
## Concordance= 0.598 (se = 0.039 )
## Likelihood ratio test= 380.5 on 59 df, p=<2e-16
## Wald test = 1.84 on 59 df, p=1
## Score (logrank) test = 374.8 on 59 df, p=<2e-16, Robust = 3 p=1
##
## (Note: the likelihood ratio and score tests assume independence of
## observations within a cluster, the Wald and robust score tests do not).
Multi-pollutant model without other covariates aside from dx_yr and site
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr, data=Simm_IPF)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr, data = Simm_IPF)
##
## coef exp(coef) se(coef) z p
## SO4 0.16647 1.18113 0.21059 0.791 0.429233
## NH4 -0.25333 0.77621 0.68553 -0.370 0.711728
## NO3 -0.01976 0.98043 0.32281 -0.061 0.951185
## BC 0.48917 1.63097 0.52926 0.924 0.355355
## OM 0.22136 1.24777 0.11729 1.887 0.059130
## SS 1.05065 2.85952 0.43253 2.429 0.015137
## Soil -1.54957 0.21234 0.42420 -3.653 0.000259
## dx_yr 0.04649 1.04759 0.02511 1.851 0.064118
##
## Likelihood ratio test=36.25 on 8 df, p=1.58e-05
## n= 31982, number of events= 695
## (446 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit1 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=Simm_IPF[,c(Xnm, 'start', 'end', 'event', 'dx_yr')], q=4)
qc.survfit1
## Scaled effect size (positive direction, sum of positive coefficients = 0.432)
## OM NH4 SS SO4
## 0.401 0.241 0.211 0.147
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.226)
## NO3 Soil BC
## 0.5069 0.4529 0.0402
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 0.205126 0.096256 0.016467 0.39378 2.131 0.03309
#Lastly the HR is reported through the following
exp(qc.survfit1$coef)
## psi1
## 1.22768
exp(qc.survfit1$ci)
## [1] 1.016604 1.482581
Now to plot the findings
plot(qc.survfit1)
Complete multi-pollutant model + dx_yr
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex + smokeHx + dich_Race + disadv + dx_yr, data=Simm_IPF)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex +
## smokeHx + dich_Race + disadv + dx_yr, data = Simm_IPF)
##
## coef exp(coef) se(coef) z p
## SO4 0.015051 1.015164 0.224522 0.067 0.946555
## NH4 0.120737 1.128328 0.731716 0.165 0.868940
## NO3 -0.205502 0.814238 0.350773 -0.586 0.557973
## BC 0.419398 1.521046 0.578392 0.725 0.468384
## OM 0.295929 1.344375 0.137109 2.158 0.030901
## SS 0.998684 2.714708 0.469840 2.126 0.033538
## Soil -1.733707 0.176628 0.450447 -3.849 0.000119
## age_dx 0.001466 1.001467 0.004609 0.318 0.750480
## sexF -0.366589 0.693094 0.090010 -4.073 4.65e-05
## smokeHxFormer 0.079434 1.082674 0.092474 0.859 0.390352
## smokeHxAlways -0.586083 0.556503 0.276240 -2.122 0.033868
## smokeHxUnknown 0.396254 1.486246 0.174162 2.275 0.022894
## dich_RaceNon-White 0.132498 1.141677 0.129948 1.020 0.307906
## disadv 0.489431 1.631387 0.138044 3.545 0.000392
## dx_yr 0.028887 1.029309 0.027007 1.070 0.284781
##
## Likelihood ratio test=80.06 on 15 df, p=6.815e-11
## n= 30488, number of events= 670
## (1940 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit2 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=Simm_IPF[,c(Xnm, 'age_dx', 'sex', 'smokeHx', 'dich_Race', 'disadv', 'start', 'end', 'event', 'dx_yr')], q=4)
qc.survfit2
## Scaled effect size (positive direction, sum of positive coefficients = 0.37)
## OM NH4 SS SO4
## 0.522 0.285 0.160 0.033
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.229)
## Soil NO3 BC
## 0.52211 0.47314 0.00475
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 0.14081 0.10198 -0.059076 0.34069 1.3807 0.1674
#Lastly the HR is reported through the following
exp(qc.survfit2$coef)
## psi1
## 1.151204
exp(qc.survfit2$ci)
## [1] 0.9426353 1.4059199
Now to plot the findings
plot(qc.survfit2)
Multi-pollutant model without other covariates aside from dx_yr and site
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr + site, data=PFF_IPF)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr + site,
## data = PFF_IPF)
##
## coef exp(coef) se(coef) z p
## SO4 0.086761 1.090636 0.262582 0.330 0.74109
## NH4 0.975197 2.651689 0.613475 1.590 0.11192
## NO3 -0.655077 0.519402 0.222873 -2.939 0.00329
## BC -0.300052 0.740780 0.480852 -0.624 0.53263
## OM 0.218479 1.244183 0.103502 2.111 0.03478
## SS 0.122207 1.129988 0.157303 0.777 0.43722
## Soil -0.374684 0.687507 0.266112 -1.408 0.15913
## dx_yr 0.170230 1.185578 0.025657 6.635 3.25e-11
## site02R 0.739631 2.095162 0.395186 1.872 0.06126
## site03R 0.141077 1.151513 0.391011 0.361 0.71825
## site04R 0.207725 1.230874 0.524541 0.396 0.69210
## site05R -0.562068 0.570029 0.447744 -1.255 0.20936
## site06R -0.249953 0.778837 0.393082 -0.636 0.52485
## site07R -0.157797 0.854023 0.374757 -0.421 0.67371
## site08R NA NA 0.000000 NA NA
## site09R -0.131638 0.876658 0.365699 -0.360 0.71888
## site10R 0.139767 1.150006 0.476927 0.293 0.76948
## site11R -0.362254 0.696105 0.381134 -0.950 0.34188
## site12R 0.292830 1.340215 0.379065 0.773 0.43982
## site13R -0.419414 0.657432 0.365631 -1.147 0.25134
## site14R -0.353266 0.702390 0.676924 -0.522 0.60176
## site15R -0.222997 0.800118 0.393831 -0.566 0.57124
## site16R 0.140785 1.151177 0.382385 0.368 0.71274
## site17R 0.119392 1.126812 0.431868 0.276 0.78220
## site18R -0.436953 0.646002 0.395993 -1.103 0.26984
## site19R 0.572791 1.773209 0.495391 1.156 0.24758
## site20R 0.012402 1.012479 0.410612 0.030 0.97591
## site21R -0.519529 0.594800 0.398814 -1.303 0.19268
## site22R -0.048168 0.952974 0.368078 -0.131 0.89588
## site23R -0.039590 0.961184 0.423430 -0.093 0.92551
## site24R 0.277119 1.319323 0.386556 0.717 0.47344
## site25R 0.200238 1.221693 0.384925 0.520 0.60292
## site26R -0.385992 0.679776 0.468053 -0.825 0.40956
## site27R -0.024253 0.976039 0.794356 -0.031 0.97564
## site28R -0.275965 0.758840 0.500139 -0.552 0.58110
## site29R -0.320920 0.725481 0.467091 -0.687 0.49204
## site30R 0.171423 1.186993 0.423976 0.404 0.68598
## site31R -0.513143 0.598611 0.484921 -1.058 0.28996
## site32R 0.383733 1.467754 0.585482 0.655 0.51220
## site33R -0.270238 0.763198 0.458498 -0.589 0.55559
## site34R -0.206830 0.813158 0.353295 -0.585 0.55826
## site35R -0.270013 0.763370 0.350439 -0.770 0.44100
## site36R -0.324174 0.723124 0.408824 -0.793 0.42781
## site37R -0.240132 0.786524 0.404233 -0.594 0.55248
## site38R -0.519216 0.594987 0.385036 -1.348 0.17750
## site39R 0.249465 1.283339 0.392729 0.635 0.52529
## site40R 0.660821 1.936381 0.433942 1.523 0.12780
## site41R -0.149667 0.860994 0.406070 -0.369 0.71244
## site42R 0.002361 1.002364 0.422597 0.006 0.99554
##
## Likelihood ratio test=130.9 on 48 df, p=1.302e-09
## n= 54499, number of events= 1146
## (217 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit1 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=PFF_IPF[,c(Xnm, 'start', 'end', 'event', 'dx_yr', 'site')], q=4)
qc.survfit1
## Scaled effect size (positive direction, sum of positive coefficients = 0.172)
## BC
## 1
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.321)
## NO3 Soil SO4 NH4 OM SS
## 0.45420 0.17886 0.16014 0.14257 0.06012 0.00412
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 -0.148474 0.084657 -0.3144 0.01745 -1.7538 0.07946
#Lastly the HR is reported through the following
exp(qc.survfit1$coef)
## psi1
## 0.8620221
exp(qc.survfit1$ci)
## [1] 0.7302278 1.0176031
Now to plot the findings
plot(qc.survfit1)
Complete multi-pollutant model + dx_yr
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex + smokeHx + dich_Race + disadv + dx_yr + site, data=PFF_IPF)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex +
## smokeHx + dich_Race + disadv + dx_yr + site, data = PFF_IPF)
##
## coef exp(coef) se(coef) z p
## SO4 0.0682061 1.0705859 0.2643988 0.258 0.79643
## NH4 1.0312190 2.8044824 0.6192981 1.665 0.09588
## NO3 -0.6928850 0.5001311 0.2271490 -3.050 0.00229
## BC -0.3276593 0.7206085 0.4858133 -0.674 0.50002
## OM 0.2330747 1.2624758 0.1068657 2.181 0.02918
## SS 0.1335285 1.1428538 0.1588476 0.841 0.40057
## Soil -0.3176324 0.7278703 0.2716459 -1.169 0.24229
## age_dx 0.0006209 1.0006211 0.0039585 0.157 0.87537
## sexM 0.0668837 1.0691712 0.0714739 0.936 0.34939
## smokeHxEver 0.0432869 1.0442374 0.0646577 0.669 0.50319
## dich_RaceNon-White -0.0784760 0.9245242 0.1266551 -0.620 0.53552
## disadv -0.0710134 0.9314494 0.1148516 -0.618 0.53637
## dx_yr 0.1686953 1.1837593 0.0262243 6.433 1.25e-10
## site02R 0.6495817 1.9147396 0.4064288 1.598 0.10998
## site03R 0.0477675 1.0489267 0.3999373 0.119 0.90493
## site04R 0.1506611 1.1626026 0.5334735 0.282 0.77763
## site05R -0.6315396 0.5317725 0.4605219 -1.371 0.17026
## site06R -0.3148963 0.7298646 0.4099898 -0.768 0.44245
## site07R -0.2197536 0.8027165 0.3867228 -0.568 0.56987
## site08R NA NA 0.0000000 NA NA
## site09R -0.2383156 0.7879540 0.3798596 -0.627 0.53041
## site10R 0.0197230 1.0199187 0.4887279 0.040 0.96781
## site11R -0.4790718 0.6193580 0.4007408 -1.195 0.23191
## site12R 0.2325779 1.2618487 0.3899961 0.596 0.55094
## site13R -0.4975654 0.6080091 0.3778694 -1.317 0.18792
## site14R -0.4295314 0.6508140 0.6831728 -0.629 0.52953
## site15R -0.3129155 0.7313117 0.4116435 -0.760 0.44716
## site16R 0.0814750 1.0848861 0.3926599 0.207 0.83562
## site17R 0.0361574 1.0368191 0.4446305 0.081 0.93519
## site18R -0.5295561 0.5888663 0.4115308 -1.287 0.19817
## site19R 0.4301527 1.5374923 0.5035536 0.854 0.39298
## site20R -0.0708739 0.9315794 0.4218986 -0.168 0.86659
## site21R -0.6095632 0.5435883 0.4141382 -1.472 0.14105
## site22R -0.1109382 0.8949941 0.3806988 -0.291 0.77074
## site23R -0.1693367 0.8442246 0.4362460 -0.388 0.69789
## site24R 0.2147689 1.2395755 0.3969884 0.541 0.58851
## site25R 0.1244613 1.1325381 0.3932679 0.316 0.75164
## site26R -0.4979039 0.6078034 0.4790399 -1.039 0.29863
## site27R -0.1303783 0.8777633 0.8044672 -0.162 0.87125
## site28R -0.3239281 0.7233022 0.5129817 -0.631 0.52774
## site29R -0.3988858 0.6710674 0.4769630 -0.836 0.40298
## site30R 0.1227835 1.1306396 0.4376044 0.281 0.77903
## site31R -0.5914321 0.5535340 0.4944519 -1.196 0.23164
## site32R 0.1943749 1.2145516 0.6068172 0.320 0.74873
## site33R -0.3820941 0.6824308 0.4703026 -0.812 0.41654
## site34R -0.2718159 0.7619945 0.3672039 -0.740 0.45916
## site35R -0.3593655 0.6981191 0.3656186 -0.983 0.32566
## site36R -0.3796064 0.6841306 0.4237750 -0.896 0.37037
## site37R -0.2916583 0.7470238 0.4161383 -0.701 0.48338
## site38R -0.5818764 0.5588488 0.3995920 -1.456 0.14534
## site39R 0.1481103 1.1596408 0.4014249 0.369 0.71216
## site40R 0.5907592 1.8053585 0.4440327 1.330 0.18337
## site41R -0.2109789 0.8097911 0.4162127 -0.507 0.61222
## site42R -0.0768702 0.9260100 0.4335103 -0.177 0.85926
##
## Likelihood ratio test=131.6 on 53 df, p=1.256e-08
## n= 53867, number of events= 1131
## (849 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit2 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=PFF_IPF[,c(Xnm, 'age_dx', 'sex', 'smokeHx', 'dich_Race', 'disadv', 'start', 'end', 'event', 'dx_yr', 'site')], q=4)
qc.survfit2
## Scaled effect size (positive direction, sum of positive coefficients = 0.176)
## BC
## 1
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.329)
## NO3 NH4 Soil SO4 OM SS
## 0.44361 0.18175 0.17572 0.15412 0.03593 0.00886
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 -0.152718 0.085593 -0.32048 0.015041 -1.7842 0.07439
#Lastly the HR is reported through the following
exp(qc.survfit2$coef)
## psi1
## 0.8583717
exp(qc.survfit2$ci)
## [1] 0.7258023 1.0151551
Now to plot the findings
plot(qc.survfit2)
Multi-pollutant model without other covariates aside from dx_yr and site
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr + site, data=CARE_IPF)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr + site,
## data = CARE_IPF)
##
## coef exp(coef) se(coef) z p
## SO4 -0.78591 0.45570 0.69324 -1.134 0.25693
## NH4 -4.12997 0.01608 1.40466 -2.940 0.00328
## NO3 2.04128 7.70048 0.45594 4.477 7.57e-06
## BC 3.21733 24.96134 1.10532 2.911 0.00361
## OM -0.73315 0.48040 0.15687 -4.674 2.96e-06
## SS -0.46123 0.63050 0.53764 -0.858 0.39096
## Soil 0.99067 2.69303 0.53018 1.869 0.06169
## dx_yr 0.61189 1.84391 0.03943 15.518 < 2e-16
## site102 -0.24412 0.78339 0.28595 -0.854 0.39326
## site103 0.32255 1.38064 0.31749 1.016 0.30966
## site104 0.19787 1.21880 0.32606 0.607 0.54396
## site105 0.61776 1.85476 0.19018 3.248 0.00116
## site106 0.22905 1.25741 0.13279 1.725 0.08455
## site107 0.73374 2.08286 0.24637 2.978 0.00290
## site108 -0.99633 0.36923 0.32183 -3.096 0.00196
##
## Likelihood ratio test=1125 on 15 df, p=< 2.2e-16
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit1 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=CARE_IPF[,c(Xnm, 'start', 'end', 'event', 'dx_yr', 'site')], q=4)
qc.survfit1
## Scaled effect size (positive direction, sum of positive coefficients = 0.261)
## BC NO3 Soil
## 0.5490 0.3611 0.0898
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.509)
## OM NH4 SO4 SS
## 0.3764 0.3693 0.1956 0.0587
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 -0.248189 0.086547 -0.41782 -0.078561 -2.8677 0.004135
#Lastly the HR is reported through the following
exp(qc.survfit1$coef)
## psi1
## 0.7802122
exp(qc.survfit1$ci)
## [1] 0.6584821 0.9244459
Now to plot the findings
plot(qc.survfit1)
Complete multi-pollutant model + dx_yr
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex + smokeHx + dich_Race + disadv + dx_yr + site, data=CARE_IPF)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex +
## smokeHx + dich_Race + disadv + dx_yr + site, data = CARE_IPF)
##
## coef exp(coef) se(coef) z p
## SO4 -0.646700 0.523771 0.709768 -0.911 0.36222
## NH4 -4.348431 0.012927 1.426703 -3.048 0.00230
## NO3 2.075928 7.971944 0.463491 4.479 7.50e-06
## BC 3.211722 24.821783 1.118638 2.871 0.00409
## OM -0.737455 0.478330 0.158416 -4.655 3.24e-06
## SS -0.496937 0.608392 0.543497 -0.914 0.36054
## Soil 0.878388 2.407016 0.540870 1.624 0.10437
## age_dx 0.012854 1.012937 0.004582 2.805 0.00503
## sexF -0.036144 0.964502 0.077360 -0.467 0.64035
## smokeHxFormer -0.006665 0.993358 0.081933 -0.081 0.93517
## smokeHxAlways 0.042406 1.043318 0.170431 0.249 0.80350
## smokeHxUnknown 0.560480 1.751513 0.737086 0.760 0.44702
## dich_RaceNon-White -0.108693 0.897005 0.111837 -0.972 0.33111
## disadv -0.019298 0.980887 0.132027 -0.146 0.88379
## dx_yr 0.612491 1.845022 0.039797 15.390 < 2e-16
## site102 -0.231676 0.793203 0.293406 -0.790 0.42976
## site103 0.357804 1.430186 0.328091 1.091 0.27546
## site104 0.234442 1.264203 0.334763 0.700 0.48373
## site105 0.640986 1.898352 0.195877 3.272 0.00107
## site106 0.202590 1.224571 0.134152 1.510 0.13100
## site107 0.672944 1.960000 0.255484 2.634 0.00844
## site108 -1.015570 0.362196 0.330616 -3.072 0.00213
##
## Likelihood ratio test=1136 on 22 df, p=< 2.2e-16
## n= 36886, number of events= 908
## (60 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit2 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=CARE_IPF[,c(Xnm, 'age_dx', 'sex', 'smokeHx', 'dich_Race', 'disadv', 'start', 'end', 'event', 'dx_yr', 'site')], q=4)
qc.survfit2
## Scaled effect size (positive direction, sum of positive coefficients = 0.254)
## BC NO3 Soil
## 0.5598 0.3960 0.0442
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.511)
## NH4 OM SO4 SS
## 0.3793 0.3752 0.1834 0.0621
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 -0.257038 0.087759 -0.42904 -0.085034 -2.9289 0.003402
#Lastly the HR is reported through the following
exp(qc.survfit2$coef)
## psi1
## 0.7733387
exp(qc.survfit2$ci)
## [1] 0.6511322 0.9184813
Now to plot the findings
plot(qc.survfit2)
Multi-pollutant model without other covariates aside from dx_yr and site
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr + site + cluster(cohort), data=All_IPF)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + dx_yr + site,
## data = All_IPF, cluster = cohort)
##
## coef exp(coef) se(coef) robust se z p
## SO4 0.73035 2.07581 0.12006 0.40766 1.792 0.0732
## NH4 -0.55779 0.57247 0.36695 1.04949 -0.531 0.5951
## NO3 -0.12097 0.88606 0.15794 0.42204 -0.287 0.7744
## BC 1.05796 2.88050 0.27318 2.05141 0.516 0.6060
## OM -0.17272 0.84137 0.05788 0.39206 -0.441 0.6595
## SS 0.11210 1.11863 0.14063 0.24561 0.456 0.6481
## Soil -0.35240 0.70300 0.19642 0.20120 -1.752 0.0799
## dx_yr 0.21263 1.23693 0.01284 0.09938 2.140 0.0324
## site02R 0.41126 1.50872 0.38497 0.40873 1.006 0.3143
## site03R -0.16364 0.84905 0.37195 0.34792 -0.470 0.6381
## site04R -0.78060 0.45813 0.48445 0.72554 -1.076 0.2820
## site05R -1.28198 0.27749 0.38712 0.62990 -2.035 0.0418
## site06R -0.87299 0.41770 0.33957 0.53135 -1.643 0.1004
## site07R -0.83772 0.43270 0.33125 0.49371 -1.697 0.0897
## site09R -0.59399 0.55212 0.34646 0.33842 -1.755 0.0792
## site1 -0.96789 0.37988 0.30370 0.72637 -1.332 0.1827
## site101 -0.57829 0.56086 0.30339 0.34687 -1.667 0.0955
## site102 -0.19020 0.82680 0.30128 0.03508 -5.422 5.91e-08
## site103 -0.02096 0.97926 0.28708 0.01706 -1.229 0.2191
## site104 -0.35820 0.69893 0.29733 0.01870 -19.158 < 2e-16
## site105 -0.28071 0.75525 0.29499 0.05868 -4.784 1.72e-06
## site106 -0.71282 0.49026 0.29997 0.35878 -1.987 0.0469
## site107 -0.19773 0.82059 0.33171 0.07768 -2.545 0.0109
## site108 -0.09243 0.91172 0.37631 0.24443 -0.378 0.7053
## site10R -0.61826 0.53888 0.42835 0.49308 -1.254 0.2099
## site11R -0.93599 0.39220 0.32084 0.61491 -1.522 0.1280
## site12R -0.24707 0.78109 0.35472 0.32909 -0.751 0.4528
## site13R -1.18214 0.30662 0.31897 0.67134 -1.761 0.0783
## site14R -1.11988 0.32632 0.65283 0.57665 -1.942 0.0521
## site15R -0.84908 0.42781 0.34455 0.53351 -1.591 0.1115
## site16R -0.49679 0.60848 0.35729 0.47612 -1.043 0.2968
## site17R -0.60207 0.54768 0.38493 0.56101 -1.073 0.2832
## site18R -1.02728 0.35798 0.34154 0.55637 -1.846 0.0648
## site19R 0.19359 1.21360 0.43400 0.45682 0.424 0.6717
## site20R -0.65259 0.52069 0.37154 0.47851 -1.364 0.1726
## site21R -1.29100 0.27500 0.34304 0.79401 -1.626 0.1040
## site22R -0.86410 0.42143 0.32873 0.67724 -1.276 0.2020
## site23R -0.62165 0.53706 0.36325 0.50428 -1.233 0.2177
## site24R -0.30381 0.73800 0.35563 0.34147 -0.890 0.3736
## site25R -0.18337 0.83246 0.37300 0.38861 -0.472 0.6370
## site26R -1.05685 0.34755 0.39346 0.56193 -1.881 0.0600
## site27R -0.89743 0.40762 0.76996 0.70017 -1.282 0.1999
## site28R -1.06677 0.34412 0.45256 0.66379 -1.607 0.1080
## site29R -1.01452 0.36258 0.44411 0.48787 -2.079 0.0376
## site30R -0.85189 0.42661 0.37761 0.83729 -1.017 0.3089
## site31R -1.13490 0.32145 0.45654 0.57285 -1.981 0.0476
## site32R -0.01600 0.98413 0.50069 0.61675 -0.026 0.9793
## site33R -0.95322 0.38550 0.38280 0.61682 -1.545 0.1223
## site34R -0.73068 0.48158 0.32282 0.38565 -1.895 0.0581
## site35R -0.59488 0.55163 0.33711 0.23034 -2.583 0.0098
## site36R -0.99933 0.36813 0.35892 0.56125 -1.781 0.0750
## site37R -1.01398 0.36277 0.35760 0.63391 -1.600 0.1097
## site38R -1.13204 0.32237 0.33945 0.51400 -2.202 0.0276
## site39R -0.05302 0.94836 0.38023 0.30602 -0.173 0.8625
## site40R -0.17189 0.84207 0.40685 1.00296 -0.171 0.8639
## site41R -0.87545 0.41668 0.38396 0.64137 -1.365 0.1723
## site42R -0.80581 0.44672 0.37843 0.64566 -1.248 0.2120
##
## Likelihood ratio test=528.8 on 57 df, p=< 2.2e-16
## n= 123367, number of events= 2749
## (723 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit1 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=All_IPF[,c(Xnm, 'start', 'end', 'event', 'dx_yr', 'site', 'cohort')], q=4)
qc.survfit1
## Scaled effect size (positive direction, sum of positive coefficients = 0.31)
## BC Soil SO4
## 0.7345 0.2260 0.0396
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.365)
## NH4 OM NO3 SS
## 0.53449 0.35055 0.10544 0.00952
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 -0.054871 0.053901 -0.16051 0.050773 -1.018 0.3087
#Lastly the HR is reported through the following
exp(qc.survfit1$coef)
## psi1
## 0.9466076
exp(qc.survfit1$ci)
## [1] 0.8517055 1.0520843
Now to plot the findings
plot(qc.survfit1)
Complete multi-pollutant model
#First need to list the pollutants in the model
Xnm <- c('SO4','NO3','NH4','BC','OM','SS','Soil')
#Next construct the base cox model
survival::coxph(survival::Surv(start, end, event==1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex + smokeHx + dich_Race + disadv + dx_yr + site + cluster(cohort), data=All_IPF)
## Call:
## survival::coxph(formula = survival::Surv(start, end, event ==
## 1) ~ SO4 + NH4 + NO3 + BC + OM + SS + Soil + age_dx + sex +
## smokeHx + dich_Race + disadv + dx_yr + site, data = All_IPF,
## cluster = cohort)
##
## coef exp(coef) se(coef) robust se z p
## SO4 0.729102 2.073218 0.122054 0.413807 1.762 0.078080
## NH4 -0.526217 0.590836 0.371985 1.081748 -0.486 0.626648
## NO3 -0.116989 0.889595 0.160290 0.446826 -0.262 0.793458
## BC 1.115783 3.051957 0.277513 2.089164 0.534 0.593286
## OM -0.191679 0.825571 0.058941 0.391135 -0.490 0.624092
## SS 0.092221 1.096608 0.142145 0.236987 0.389 0.697171
## Soil -0.313375 0.730976 0.199709 0.199586 -1.570 0.116385
## age_dx 0.002221 1.002224 0.002437 0.001999 1.111 0.266453
## sexF -0.150308 0.860443 0.044975 0.084643 -1.776 0.075769
## smokeHxFormer 0.114838 1.121692 0.059665 0.016400 7.002 2.52e-12
## smokeHxAlways -0.004764 0.995248 0.138843 0.106168 -0.045 0.964211
## smokeHxUnknown 0.323062 1.381351 0.154155 0.179538 1.799 0.071954
## smokeHxEver 0.031876 1.032390 0.064116 0.006932 4.598 4.26e-06
## dich_RaceNon-White 0.026473 1.026826 0.069198 0.086931 0.305 0.760727
## disadv 0.089470 1.093594 0.072029 0.103013 0.869 0.385108
## dx_yr 0.212487 1.236749 0.013249 0.104647 2.030 0.042306
## site02R 0.403773 1.497464 0.394718 0.354074 1.140 0.254135
## site03R -0.190536 0.826516 0.380949 0.357920 -0.532 0.594489
## site04R -0.829753 0.436157 0.492570 0.747281 -1.110 0.266842
## site05R -1.330807 0.264264 0.397853 0.646559 -2.058 0.039562
## site06R -0.942449 0.389672 0.352865 0.528065 -1.785 0.074307
## site07R -0.896980 0.407799 0.342156 0.502405 -1.785 0.074201
## site09R -0.659495 0.517112 0.358616 0.338902 -1.946 0.051658
## site1 -1.106646 0.330666 0.320806 0.719791 -1.537 0.124182
## site101 -0.679735 0.506751 0.318362 0.351108 -1.936 0.052871
## site102 -0.287600 0.750062 0.316691 0.040356 -7.127 1.03e-12
## site103 -0.123630 0.883707 0.303150 0.032266 -3.832 0.000127
## site104 -0.449816 0.637745 0.313276 0.029836 -15.077 < 2e-16
## site105 -0.379158 0.684438 0.312583 0.058970 -6.430 1.28e-10
## site106 -0.842435 0.430660 0.315643 0.363728 -2.316 0.020552
## site107 -0.320169 0.726026 0.347582 0.081596 -3.924 8.71e-05
## site108 -0.213308 0.807907 0.388678 0.247231 -0.863 0.388255
## site10R -0.715393 0.489000 0.439085 0.460421 -1.554 0.120237
## site11R -1.009486 0.364406 0.335433 0.621776 -1.624 0.104472
## site12R -0.317514 0.727956 0.365021 0.341618 -0.929 0.352660
## site13R -1.253239 0.285578 0.330622 0.680305 -1.842 0.065450
## site14R -1.162674 0.312649 0.658714 0.597193 -1.947 0.051547
## site15R -0.918264 0.399211 0.357475 0.524733 -1.750 0.080125
## site16R -0.538755 0.583474 0.367666 0.473905 -1.137 0.255604
## site17R -0.679300 0.506972 0.395713 0.573417 -1.185 0.236155
## site18R -1.102539 0.332027 0.353195 0.542070 -2.034 0.041957
## site19R 0.083009 1.086552 0.443305 0.441326 0.188 0.850806
## site20R -0.703426 0.494887 0.382051 0.483568 -1.455 0.145764
## site21R -1.365772 0.255184 0.355168 0.800215 -1.707 0.087867
## site22R -0.919568 0.398691 0.340847 0.696206 -1.321 0.186559
## site23R -0.736183 0.478939 0.375571 0.488699 -1.506 0.131961
## site24R -0.356308 0.700257 0.366199 0.346409 -1.029 0.303678
## site25R -0.195842 0.822142 0.381138 0.330353 -0.593 0.553297
## site26R -1.156147 0.314697 0.402971 0.532325 -2.172 0.029865
## site27R -0.939588 0.390789 0.777294 0.685246 -1.371 0.170322
## site28R -1.124445 0.324833 0.462113 0.682983 -1.646 0.099687
## site29R -1.033247 0.355849 0.453196 0.504043 -2.050 0.040372
## site30R -0.968753 0.379556 0.390676 0.871411 -1.112 0.266264
## site31R -1.201218 0.300828 0.464993 0.575538 -2.087 0.036877
## site32R -0.168618 0.844831 0.519749 0.624164 -0.270 0.787044
## site33R -1.047076 0.350963 0.393788 0.585604 -1.788 0.073772
## site34R -0.774278 0.461037 0.335019 0.396589 -1.952 0.050898
## site35R -0.655435 0.519216 0.350174 0.225141 -2.911 0.003600
## site36R -1.059422 0.346656 0.370409 0.585160 -1.810 0.070221
## site37R -1.078461 0.340118 0.368640 0.660571 -1.633 0.102549
## site38R -1.177667 0.307996 0.351464 0.504588 -2.334 0.019600
## site39R -0.134541 0.874117 0.390189 0.315008 -0.427 0.669303
## site40R -0.288640 0.749282 0.417646 1.030048 -0.280 0.779309
## site41R -0.960294 0.382780 0.393877 0.656131 -1.464 0.143311
## site42R -0.869892 0.418997 0.388589 0.651225 -1.336 0.181622
##
## Likelihood ratio test=561.7 on 65 df, p=< 2.2e-16
## n= 121241, number of events= 2709
## (2849 observations deleted due to missingness)
#Next the quantile regression model
qc.survfit2 <- qgcomp.cox.noboot(survival::Surv(start, end, event==1) ~ ., expnms=Xnm, data=All_IPF[,c(Xnm, 'age_dx', 'sex', 'smokeHx', 'dich_Race', 'disadv', 'start', 'end', 'event', 'dx_yr', 'site', 'cohort')], q=4)
qc.survfit2
## Scaled effect size (positive direction, sum of positive coefficients = 0.311)
## BC Soil SO4
## 0.7234 0.2278 0.0488
##
## Scaled effect size (negative direction, sum of negative coefficients = -0.371)
## NH4 OM NO3 SS
## 0.5239 0.3485 0.1031 0.0245
##
## Mixture log(hazard ratio) (Delta method CI):
##
## Estimate Std. Error Lower CI Upper CI Z value Pr(>|z|)
## psi1 -0.060520 0.054604 -0.16754 0.046501 -1.1084 0.2677
#Lastly the HR is reported through the following
exp(qc.survfit2$coef)
## psi1
## 0.941275
exp(qc.survfit2$ci)
## [1] 0.845742 1.047599
Now to plot the findings
plot(qc.survfit2)